353 lines
11 KiB
Python
353 lines
11 KiB
Python
from .base import _get_response
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from .. import auc
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from .. import roc_curve
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from .._base import _check_pos_label_consistency
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from ...utils import check_matplotlib_support
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class RocCurveDisplay:
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"""ROC Curve visualization.
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It is recommend to use
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:func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or
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:func:`~sklearn.metrics.RocCurveDisplay.from_predictions` to create
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a :class:`~sklearn.metrics.RocCurveDisplay`. All parameters are
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stored as attributes.
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Read more in the :ref:`User Guide <visualizations>`.
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Parameters
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----------
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fpr : ndarray
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False positive rate.
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tpr : ndarray
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True positive rate.
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roc_auc : float, default=None
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Area under ROC curve. If None, the roc_auc score is not shown.
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estimator_name : str, default=None
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Name of estimator. If None, the estimator name is not shown.
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pos_label : str or int, default=None
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The class considered as the positive class when computing the roc auc
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metrics. By default, `estimators.classes_[1]` is considered
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as the positive class.
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.. versionadded:: 0.24
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Attributes
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----------
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line_ : matplotlib Artist
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ROC Curve.
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ax_ : matplotlib Axes
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Axes with ROC Curve.
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figure_ : matplotlib Figure
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Figure containing the curve.
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See Also
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--------
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roc_curve : Compute Receiver operating characteristic (ROC) curve.
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RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
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(ROC) curve given an estimator and some data.
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RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
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(ROC) curve given the true and predicted values.
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roc_auc_score : Compute the area under the ROC curve.
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Examples
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--------
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>>> import matplotlib.pyplot as plt
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>>> import numpy as np
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>>> from sklearn import metrics
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>>> y = np.array([0, 0, 1, 1])
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>>> pred = np.array([0.1, 0.4, 0.35, 0.8])
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>>> fpr, tpr, thresholds = metrics.roc_curve(y, pred)
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>>> roc_auc = metrics.auc(fpr, tpr)
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>>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
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... estimator_name='example estimator')
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>>> display.plot()
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<...>
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>>> plt.show()
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"""
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def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None):
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self.estimator_name = estimator_name
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self.fpr = fpr
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self.tpr = tpr
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self.roc_auc = roc_auc
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self.pos_label = pos_label
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def plot(self, ax=None, *, name=None, **kwargs):
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"""Plot visualization.
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Extra keyword arguments will be passed to matplotlib's ``plot``.
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Parameters
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----------
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ax : matplotlib axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is
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created.
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name : str, default=None
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Name of ROC Curve for labeling. If `None`, use `estimator_name` if
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not `None`, otherwise no labeling is shown.
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**kwargs : dict
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Keyword arguments to be passed to matplotlib's `plot`.
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Returns
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-------
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display : :class:`~sklearn.metrics.plot.RocCurveDisplay`
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Object that stores computed values.
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"""
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check_matplotlib_support("RocCurveDisplay.plot")
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name = self.estimator_name if name is None else name
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line_kwargs = {}
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if self.roc_auc is not None and name is not None:
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line_kwargs["label"] = f"{name} (AUC = {self.roc_auc:0.2f})"
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elif self.roc_auc is not None:
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line_kwargs["label"] = f"AUC = {self.roc_auc:0.2f}"
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elif name is not None:
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line_kwargs["label"] = name
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line_kwargs.update(**kwargs)
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import matplotlib.pyplot as plt
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if ax is None:
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fig, ax = plt.subplots()
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(self.line_,) = ax.plot(self.fpr, self.tpr, **line_kwargs)
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info_pos_label = (
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f" (Positive label: {self.pos_label})" if self.pos_label is not None else ""
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)
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xlabel = "False Positive Rate" + info_pos_label
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ylabel = "True Positive Rate" + info_pos_label
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ax.set(xlabel=xlabel, ylabel=ylabel)
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if "label" in line_kwargs:
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ax.legend(loc="lower right")
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self.ax_ = ax
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self.figure_ = ax.figure
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return self
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@classmethod
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def from_estimator(
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cls,
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estimator,
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X,
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y,
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*,
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sample_weight=None,
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drop_intermediate=True,
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response_method="auto",
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pos_label=None,
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name=None,
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ax=None,
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**kwargs,
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):
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"""Create a ROC Curve display from an estimator.
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Parameters
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----------
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estimator : estimator instance
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Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
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in which the last estimator is a classifier.
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Input values.
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y : array-like of shape (n_samples,)
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Target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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drop_intermediate : bool, default=True
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Whether to drop some suboptimal thresholds which would not appear
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on a plotted ROC curve. This is useful in order to create lighter
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ROC curves.
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response_method : {'predict_proba', 'decision_function', 'auto'} \
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default='auto'
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Specifies whether to use :term:`predict_proba` or
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:term:`decision_function` as the target response. If set to 'auto',
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:term:`predict_proba` is tried first and if it does not exist
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:term:`decision_function` is tried next.
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pos_label : str or int, default=None
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The class considered as the positive class when computing the roc auc
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metrics. By default, `estimators.classes_[1]` is considered
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as the positive class.
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name : str, default=None
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Name of ROC Curve for labeling. If `None`, use the name of the
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estimator.
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ax : matplotlib axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is created.
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**kwargs : dict
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Keyword arguments to be passed to matplotlib's `plot`.
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Returns
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-------
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display : :class:`~sklearn.metrics.plot.RocCurveDisplay`
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The ROC Curve display.
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See Also
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--------
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roc_curve : Compute Receiver operating characteristic (ROC) curve.
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RocCurveDisplay.from_predictions : ROC Curve visualization given the
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probabilities of scores of a classifier.
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roc_auc_score : Compute the area under the ROC curve.
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Examples
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--------
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>>> import matplotlib.pyplot as plt
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>>> from sklearn.datasets import make_classification
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>>> from sklearn.metrics import RocCurveDisplay
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>>> from sklearn.model_selection import train_test_split
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>>> from sklearn.svm import SVC
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>>> X, y = make_classification(random_state=0)
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>>> X_train, X_test, y_train, y_test = train_test_split(
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... X, y, random_state=0)
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>>> clf = SVC(random_state=0).fit(X_train, y_train)
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>>> RocCurveDisplay.from_estimator(
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... clf, X_test, y_test)
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<...>
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>>> plt.show()
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"""
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check_matplotlib_support(f"{cls.__name__}.from_estimator")
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name = estimator.__class__.__name__ if name is None else name
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y_pred, pos_label = _get_response(
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X,
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estimator,
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response_method=response_method,
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pos_label=pos_label,
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)
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return cls.from_predictions(
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y_true=y,
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y_pred=y_pred,
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sample_weight=sample_weight,
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drop_intermediate=drop_intermediate,
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name=name,
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ax=ax,
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pos_label=pos_label,
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**kwargs,
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)
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@classmethod
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def from_predictions(
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cls,
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y_true,
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y_pred,
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*,
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sample_weight=None,
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drop_intermediate=True,
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pos_label=None,
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name=None,
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ax=None,
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**kwargs,
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):
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"""Plot ROC curve given the true and predicted values.
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Read more in the :ref:`User Guide <visualizations>`.
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.. versionadded:: 1.0
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Parameters
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----------
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y_true : array-like of shape (n_samples,)
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True labels.
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y_pred : array-like of shape (n_samples,)
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Target scores, can either be probability estimates of the positive
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class, confidence values, or non-thresholded measure of decisions
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(as returned by “decision_function” on some classifiers).
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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drop_intermediate : bool, default=True
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Whether to drop some suboptimal thresholds which would not appear
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on a plotted ROC curve. This is useful in order to create lighter
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ROC curves.
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pos_label : str or int, default=None
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The label of the positive class. When `pos_label=None`, if `y_true`
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is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
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error will be raised.
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name : str, default=None
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Name of ROC curve for labeling. If `None`, name will be set to
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`"Classifier"`.
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ax : matplotlib axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is
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created.
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**kwargs : dict
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Additional keywords arguments passed to matplotlib `plot` function.
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Returns
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-------
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display : :class:`~sklearn.metrics.RocCurveDisplay`
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Object that stores computed values.
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See Also
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--------
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roc_curve : Compute Receiver operating characteristic (ROC) curve.
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RocCurveDisplay.from_estimator : ROC Curve visualization given an
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estimator and some data.
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roc_auc_score : Compute the area under the ROC curve.
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Examples
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--------
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>>> import matplotlib.pyplot as plt
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>>> from sklearn.datasets import make_classification
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>>> from sklearn.metrics import RocCurveDisplay
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>>> from sklearn.model_selection import train_test_split
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>>> from sklearn.svm import SVC
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>>> X, y = make_classification(random_state=0)
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>>> X_train, X_test, y_train, y_test = train_test_split(
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... X, y, random_state=0)
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>>> clf = SVC(random_state=0).fit(X_train, y_train)
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>>> y_pred = clf.decision_function(X_test)
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>>> RocCurveDisplay.from_predictions(
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... y_test, y_pred)
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<...>
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>>> plt.show()
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"""
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check_matplotlib_support(f"{cls.__name__}.from_predictions")
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fpr, tpr, _ = roc_curve(
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y_true,
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y_pred,
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pos_label=pos_label,
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sample_weight=sample_weight,
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drop_intermediate=drop_intermediate,
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)
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roc_auc = auc(fpr, tpr)
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name = "Classifier" if name is None else name
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pos_label = _check_pos_label_consistency(pos_label, y_true)
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viz = RocCurveDisplay(
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fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=name, pos_label=pos_label
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)
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return viz.plot(ax=ax, name=name, **kwargs)
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