505 lines
17 KiB
Python
505 lines
17 KiB
Python
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from collections import Counter
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from ...utils._plotting import _BinaryClassifierCurveDisplayMixin
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from .._ranking import average_precision_score, precision_recall_curve
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class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin):
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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.PrecisionRecallDisplay`. 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 : int, float, bool or str, 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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prevalence_pos_label : float, default=None
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The prevalence of the positive label. It is used for plotting the
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chance level line. If None, the chance level line will not be plotted
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even if `plot_chance_level` is set to True when plotting.
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.. versionadded:: 1.3
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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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chance_level_ : matplotlib Artist or None
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The chance level line. It is `None` if the chance level is not plotted.
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.. versionadded:: 1.3
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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_score`) 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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prevalence_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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self.prevalence_pos_label = prevalence_pos_label
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def plot(
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self,
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ax=None,
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*,
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name=None,
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plot_chance_level=False,
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chance_level_kw=None,
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**kwargs,
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):
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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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plot_chance_level : bool, default=False
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Whether to plot the chance level. The chance level is the prevalence
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of the positive label computed from the data passed during
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:meth:`from_estimator` or :meth:`from_predictions` call.
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.. versionadded:: 1.3
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chance_level_kw : dict, default=None
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Keyword arguments to be passed to matplotlib's `plot` for rendering
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the chance level line.
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.. versionadded:: 1.3
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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_score`)
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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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self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=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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(self.line_,) = self.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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self.ax_.set(
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xlabel=xlabel,
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xlim=(-0.01, 1.01),
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ylabel=ylabel,
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ylim=(-0.01, 1.01),
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aspect="equal",
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)
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if plot_chance_level:
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if self.prevalence_pos_label is None:
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raise ValueError(
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"You must provide prevalence_pos_label when constructing the "
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"PrecisionRecallDisplay object in order to plot the chance "
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"level line. Alternatively, you may use "
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"PrecisionRecallDisplay.from_estimator or "
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"PrecisionRecallDisplay.from_predictions "
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"to automatically set prevalence_pos_label"
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)
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chance_level_line_kw = {
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"label": f"Chance level (AP = {self.prevalence_pos_label:0.2f})",
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"color": "k",
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"linestyle": "--",
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}
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if chance_level_kw is not None:
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chance_level_line_kw.update(chance_level_kw)
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(self.chance_level_,) = self.ax_.plot(
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(0, 1),
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(self.prevalence_pos_label, self.prevalence_pos_label),
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**chance_level_line_kw,
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)
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else:
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self.chance_level_ = None
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if "label" in line_kwargs or plot_chance_level:
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self.ax_.legend(loc="lower left")
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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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drop_intermediate=False,
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response_method="auto",
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name=None,
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ax=None,
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plot_chance_level=False,
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chance_level_kw=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 : int, float, bool or str, 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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drop_intermediate : bool, default=False
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Whether to drop some suboptimal thresholds which would not appear
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on a plotted precision-recall curve. This is useful in order to
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create lighter precision-recall curves.
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.. versionadded:: 1.3
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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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plot_chance_level : bool, default=False
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Whether to plot the chance level. The chance level is the prevalence
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of the positive label computed from the data passed during
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:meth:`from_estimator` or :meth:`from_predictions` call.
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.. versionadded:: 1.3
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chance_level_kw : dict, default=None
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Keyword arguments to be passed to matplotlib's `plot` for rendering
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the chance level line.
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.. versionadded:: 1.3
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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_score`)
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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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y_pred, pos_label, name = cls._validate_and_get_response_values(
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estimator,
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X,
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y,
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response_method=response_method,
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pos_label=pos_label,
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name=name,
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)
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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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drop_intermediate=drop_intermediate,
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ax=ax,
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plot_chance_level=plot_chance_level,
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chance_level_kw=chance_level_kw,
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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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drop_intermediate=False,
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name=None,
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ax=None,
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plot_chance_level=False,
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chance_level_kw=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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|
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sample_weight : array-like of shape (n_samples,), default=None
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|
Sample weights.
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|
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pos_label : int, float, bool or str, 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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|
|
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|
|
drop_intermediate : bool, default=False
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|
|
Whether to drop some suboptimal thresholds which would not appear
|
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|
|
on a plotted precision-recall curve. This is useful in order to
|
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|
|
create lighter precision-recall curves.
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|
|
|
||
|
|
.. versionadded:: 1.3
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|
|
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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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|
|
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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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|
|
|
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|
|
plot_chance_level : bool, default=False
|
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|
|
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
|
||
|
|
|
||
|
|
**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,
|
||
|
|
**kwargs,
|
||
|
|
)
|