RocCurveDisplay#

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

ROC Curve visualization.

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

Parameters:

fprdict of list of ndarray

False positive rate. The structure is:

for binary classification:
- the key is the positive label.
- the value is a list of ndarray, each ndarray being the false positive rate.
for multiclass classification:
- the key is the class of interest in an OvR fashion.
- the value is a list of ndarray, each ndarray being the false positive rate.

tprdict of list of ndarray

True positive rate. The structure is:

for binary classification:
- the key is the positive label
- the value is a list of ndarray, each ndarray being the true positive rate.
for multiclass classification:
- the key is the class of interest in an OvR fashion.
- the value is a list of ndarray, each ndarray being the true positive rate.

roc_aucdict of list of float

Area under the ROC curve. The structure is:

for binary classification:
- the key is the positive label
- the value is a list of float, each float being the area under the ROC curve.
for multiclass classification:
- the key is the class of interest in an OvR fashion.
- the value is a list of float, each float being the area under the ROC curve.

estimator_nameslist of str

Name of the estimators.

pos_labelint, float, bool, str or None

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

data_source{“train”, “test”, “X_y”}

The data source used to compute the ROC curve.

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

The machine learning task.

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

The type of report.

Attributes:

ax_matplotlib axes: The axes on which the ROC curve is plotted.
figure_matplotlib figure: The figure on which the ROC curve is plotted.
lines_list of matplotlib lines: The lines of the ROC curve.
chance_level_matplotlib line: The chance level line.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from skore import EstimatorReport
>>> X_train, X_test, y_train, y_test = train_test_split(
...     *load_breast_cancer(return_X_y=True), random_state=0
... )
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> display = report.metrics.roc()
>>> display.plot(roc_curve_kwargs={"color": "tab:red"})

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

plot(ax=None, *, estimator_name=None, roc_curve_kwargs=None, plot_chance_level=True, chance_level_kwargs=None, despine=True)[source]#

Plot visualization.

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

Parameters:

axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
estimator_namestr, default=None: Name of the estimator used to plot the ROC curve. If None, we use the inferred name from the estimator.
roc_curve_kwargsdict or list of dict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the ROC curve(s).
plot_chance_levelbool, default=True: Whether to plot the chance level.
chance_level_kwargsdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.
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 sklearn.model_selection import train_test_split
>>> from skore import EstimatorReport
>>> X_train, X_test, y_train, y_test = train_test_split(
...     *load_breast_cancer(return_X_y=True), random_state=0
... )
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> display = report.metrics.roc()
>>> display.plot(roc_curve_kwargs={"color": "tab:red"})