394 lines
13 KiB
Python
394 lines
13 KiB
Python
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from sklearn.base import is_classifier
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from .base import _get_response
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from .. import average_precision_score
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from .. import precision_recall_curve
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from .._base import _check_pos_label_consistency
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from .._classification import check_consistent_length
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from ...utils import check_matplotlib_support
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class PrecisionRecallDisplay:
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"""Precision Recall visualization.
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It is recommend to use
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:func:`~sklearn.metrics.PrecisionRecallDisplay.from_estimator` or
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:func:`~sklearn.metrics.PrecisionRecallDisplay.from_predictions` to create
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a :class:`~sklearn.metrics.PredictionRecallDisplay`. 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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precision : ndarray
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Precision values.
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recall : ndarray
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Recall values.
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average_precision : float, default=None
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Average precision. If None, the average precision is not shown.
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estimator_name : str, default=None
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Name of estimator. If None, then 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. If None, the class will not
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be shown in the legend.
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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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Precision recall curve.
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ax_ : matplotlib Axes
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Axes with precision recall 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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precision_recall_curve : Compute precision-recall pairs for different
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probability thresholds.
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PrecisionRecallDisplay.from_estimator : Plot Precision Recall Curve given
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a binary classifier.
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PrecisionRecallDisplay.from_predictions : Plot Precision Recall Curve
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using predictions from a binary classifier.
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Notes
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-----
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The average precision (cf. :func:`~sklearn.metrics.average_precision`) in
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scikit-learn is computed without any interpolation. To be consistent with
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this metric, the precision-recall curve is plotted without any
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interpolation as well (step-wise style).
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You can change this style by passing the keyword argument
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`drawstyle="default"` in :meth:`plot`, :meth:`from_estimator`, or
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:meth:`from_predictions`. However, the curve will not be strictly
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consistent with the reported average precision.
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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 (precision_recall_curve,
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... PrecisionRecallDisplay)
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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(X, y,
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... random_state=0)
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>>> clf = SVC(random_state=0)
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>>> clf.fit(X_train, y_train)
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SVC(random_state=0)
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>>> predictions = clf.predict(X_test)
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>>> precision, recall, _ = precision_recall_curve(y_test, predictions)
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>>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
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>>> disp.plot()
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<...>
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>>> plt.show()
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"""
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def __init__(
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self,
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precision,
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recall,
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*,
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average_precision=None,
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estimator_name=None,
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pos_label=None,
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):
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self.estimator_name = estimator_name
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self.precision = precision
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self.recall = recall
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self.average_precision = average_precision
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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 precision recall curve for labeling. If `None`, use
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`estimator_name` if 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.PrecisionRecallDisplay`
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Object that stores computed values.
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Notes
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-----
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The average precision (cf. :func:`~sklearn.metrics.average_precision`)
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in scikit-learn is computed without any interpolation. To be consistent
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with this metric, the precision-recall curve is plotted without any
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interpolation as well (step-wise style).
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You can change this style by passing the keyword argument
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`drawstyle="default"`. However, the curve will not be strictly
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consistent with the reported average precision.
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"""
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check_matplotlib_support("PrecisionRecallDisplay.plot")
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name = self.estimator_name if name is None else name
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line_kwargs = {"drawstyle": "steps-post"}
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if self.average_precision is not None and name is not None:
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line_kwargs["label"] = f"{name} (AP = {self.average_precision:0.2f})"
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elif self.average_precision is not None:
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line_kwargs["label"] = f"AP = {self.average_precision: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.recall, self.precision, **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 = "Recall" + info_pos_label
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ylabel = "Precision" + 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 left")
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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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pos_label=None,
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response_method="auto",
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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 precision-recall curve given an estimator and some data.
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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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pos_label : str or int, default=None
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The class considered as the positive class when computing the
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precision and recall metrics. By default, `estimators.classes_[1]`
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is considered as the positive class.
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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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name : str, default=None
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Name for labeling curve. If `None`, no name is used.
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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.PrecisionRecallDisplay`
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See Also
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--------
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PrecisionRecallDisplay.from_predictions : Plot precision-recall curve
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using estimated probabilities or output of decision function.
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Notes
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-----
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The average precision (cf. :func:`~sklearn.metrics.average_precision`)
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in scikit-learn is computed without any interpolation. To be consistent
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with this metric, the precision-recall curve is plotted without any
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interpolation as well (step-wise style).
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You can change this style by passing the keyword argument
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`drawstyle="default"`. However, the curve will not be strictly
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consistent with the reported average precision.
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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 PrecisionRecallDisplay
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>>> from sklearn.model_selection import train_test_split
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>>> from sklearn.linear_model import LogisticRegression
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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 = LogisticRegression()
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>>> clf.fit(X_train, y_train)
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LogisticRegression()
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>>> PrecisionRecallDisplay.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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method_name = f"{cls.__name__}.from_estimator"
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check_matplotlib_support(method_name)
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if not is_classifier(estimator):
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raise ValueError(f"{method_name} only supports classifiers")
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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,
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pos_label=pos_label,
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)
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name = name if name is not None else estimator.__class__.__name__
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return cls.from_predictions(
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y,
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y_pred,
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sample_weight=sample_weight,
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name=name,
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pos_label=pos_label,
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ax=ax,
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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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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 precision-recall curve given binary class predictions.
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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 binary labels.
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y_pred : array-like of shape (n_samples,)
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Estimated probabilities or output of decision function.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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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
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precision and recall metrics.
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name : str, default=None
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Name for labeling curve. 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 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.PrecisionRecallDisplay`
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See Also
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--------
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PrecisionRecallDisplay.from_estimator : Plot precision-recall curve
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using an estimator.
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Notes
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|
|
-----
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|
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The average precision (cf. :func:`~sklearn.metrics.average_precision`)
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|
|
in scikit-learn is computed without any interpolation. To be consistent
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|
|
with this metric, the precision-recall curve is plotted without any
|
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|
|
interpolation as well (step-wise style).
|
||
|
|
|
||
|
|
You can change this style by passing the keyword argument
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|
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`drawstyle="default"`. However, the curve will not be strictly
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consistent with the reported average precision.
|
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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 PrecisionRecallDisplay
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>>> from sklearn.model_selection import train_test_split
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>>> from sklearn.linear_model import LogisticRegression
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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 = LogisticRegression()
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>>> clf.fit(X_train, y_train)
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LogisticRegression()
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>>> y_pred = clf.predict_proba(X_test)[:, 1]
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>>> PrecisionRecallDisplay.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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check_consistent_length(y_true, y_pred, sample_weight)
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pos_label = _check_pos_label_consistency(pos_label, y_true)
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precision, recall, _ = precision_recall_curve(
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y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight
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)
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average_precision = average_precision_score(
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y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight
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)
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name = name if name is not None else "Classifier"
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viz = PrecisionRecallDisplay(
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precision=precision,
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recall=recall,
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average_precision=average_precision,
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estimator_name=name,
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||
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pos_label=pos_label,
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|
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)
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|
|
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return viz.plot(ax=ax, name=name, **kwargs)
|