PrecisionRecallCurveDisplay#

class skore.PrecisionRecallCurveDisplay(*, precision_recall, average_precision, pos_label, data_source, ml_task, report_type)[source]#

Precision Recall visualization.

An instance of this class is should created by EstimatorReport.metrics.precision_recall(). You should not create an instance of this class directly.

Parameters:

precision_recallDataFrame

The precision-recall curve data to display. The columns are

“estimator_name”
“split_index” (may be null)
“label”
“threshold”
“precision”
“recall”.

average_precisionDataFrame

The average precision data to display. The columns are

“estimator_name”
“split_index” (may be null)
“label”
“average_precision”.

pos_labelint, float, bool, str or None

The class considered as the positive class. If None, the class will not be shown in the legend.

data_source{“train”, “test”, “X_y”}

The data source used to compute the precision recall curve.

ml_task{“binary-classification”, “multiclass-classification”}

The machine learning task.

report_type{“comparison-cross-validation”, “comparison-estimator”, “cross-validation”, “estimator”}

The type of report.

Attributes:

ax_matplotlib axes or ndarray of axes: The axes on which the precision-recall curve is plotted.
figure_matplotlib figure: The figure on which the precision-recall curve is plotted.
lines_list of matplotlib lines: The lines of the precision-recall curve.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.metrics.precision_recall()
>>> display.plot(pr_curve_kwargs={"color": "tab:red"})

frame(with_average_precision=False)[source]#

Get the data used to create the precision-recall curve plot.

Parameters:

with_average_precisionbool, default=False: Whether to include the average precision column in the returned DataFrame.

Returns:

DataFrame

A DataFrame containing the precision-recall curve data with columns depending on the report type:

estimator_name: Name of the estimator (when comparing estimators)
split_index: Cross-validation fold ID (when doing cross-validation)
label: Class label (for multiclass-classification)
threshold: Decision threshold
precision: Precision score at threshold
recall: Recall score at threshold
average_precision: average precision (when with_average_precision=True)

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split, EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> clf = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(clf, **split_data)
>>> display = report.metrics.precision_recall()
>>> df = display.frame()

help()[source]#: Display available attributes and methods using rich.

plot(*, estimator_name=None, pr_curve_kwargs=None, despine=True)[source]#

Plot visualization.

Extra keyword arguments will be passed to matplotlib’s plot.

Parameters:

estimator_namestr, default=None: Name of the estimator used to plot the precision-recall curve. If None, we use the inferred name from the estimator.
pr_curve_kwargsdict or list of dict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the precision-recall curve(s).
despinebool, default=True: Whether to remove the top and right spines from the plot.

Notes

The average precision (cf. 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

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.metrics.precision_recall()
>>> display.plot(pr_curve_kwargs={"color": "tab:red"})

set_style(**kwargs)[source]#

Set the style parameters for the display.

Parameters:

**kwargsdict: Style parameters to set. Each parameter name should correspond to a a style attribute passed to the plot method of the display.

Returns:

selfobject: Returns the instance itself.

Raises:

ValueError: If a style parameter is unknown.

static style_plot(plot_func)[source]#

Apply consistent style to skore displays.

This decorator: 1. Applies default style settings 2. Executes plot_func 3. Applies tight_layout

Parameters:

plot_funccallable: The plot function to be decorated.

Returns:

callable: The decorated plot function.

PrecisionRecallCurveDisplay#

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