RocCurveDisplay#

class skore.RocCurveDisplay(*, roc_curve, roc_auc, pos_label, data_source, ml_task, report_type)[source]#

ROC Curve visualization.

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

Parameters:
roc_curveDataFrame

The ROC curve data to display. The columns are

  • estimator

  • split (may be null)

  • label

  • threshold

  • fpr

  • tpr.

roc_aucDataFrame

The ROC AUC data to display. The columns are

  • estimator

  • split (may be null)

  • label

  • roc_auc.

pos_labelint, float, bool, str or None

The class considered as positive. Only meaningful for binary classification.

data_source{“train”, “test”, “X_y”, “both”}

The data source used to compute the ROC 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:
facet_seaborn FacetGrid

FacetGrid containing the ROC curve.

figure_matplotlib figure

The figure on which the ROC curve is plotted.

ax_matplotlib axes or array of axes

The axes on which the ROC curve is plotted.

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.roc()
>>> display.set_style(relplot_kwargs={"color": "tab:red"}).plot()
frame(with_roc_auc=False)[source]#

Get the data used to create the ROC curve plot.

Parameters:
with_roc_aucbool, default=False

Whether to include ROC AUC scores in the output DataFrame.

Returns:
DataFrame

A DataFrame containing the ROC curve data with columns depending on the report type:

  • estimator: Name of the estimator (when comparing estimators)

  • split: Cross-validation split ID (when doing cross-validation)

  • label: Class label (for multiclass-classification)

  • threshold: Decision threshold

  • fpr: False Positive Rate

  • tpr: True Positive Rate

  • roc_auc: Area Under the Curve (when with_roc_auc=True)

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import EstimatorReport, train_test_split
>>> 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.roc()
>>> df = display.frame()
help()[source]#

Display display help using rich or HTML.

plot(*, subplot_by='auto', plot_chance_level=True, despine=True)[source]#

Plot visualization.

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

Parameters:
subplot_by{“auto”, “label”, “estimator”, “data_source”} or None, default=”auto”

Column to use for creating subplots. Options:

  • “auto”: None for EstimatorReport and CrossValidationReport, “estimator” for ComparisonReport

  • “label”: one subplot per class (multiclass only)

  • “estimator”: one subplot per estimator (comparison only)

  • “data_source”: one subplot per data source (EstimatorReport with both data sources only)

  • None: no subplots (not available for comparison in multiclass)

plot_chance_levelbool, default=True

Whether to plot the chance level.

despinebool, default=True

Whether to remove the top and right spines from the plot.

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.roc()
>>> display.set_style(relplot_kwargs={"color": "tab:red"}).plot()
set_style(*, policy='update', relplot_kwargs=None, chance_level_kwargs=None)[source]#

Set the style parameters for the display.

Parameters:
policy{“override”, “update”}, default=”update”

Policy to use when setting the style parameters. If “override”, existing settings are set to the provided values. If “update”, existing settings are not changed; only settings that were previously unset are changed.

relplot_kwargsdict, default=None

Keyword arguments to be passed to seaborn.relplot() for rendering the ROC curve(s). Common options include palette, alpha, linewidth, etc.

chance_level_kwargsdict, default=None

Keyword arguments to be passed to matplotlib.pyplot.plot() for rendering the chance level line. Common options include color, alpha, linestyle, etc.

Returns:
selfobject

The instance with a modified style.

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. Calls plt.tight_layout() to make sure axis does not overlap 4. Restores the original style settings

Parameters:
plot_funccallable

The plot function to be decorated.

Returns:
callable

The decorated plot function.