EstimatorReport.metrics.report_metrics#

EstimatorReport.metrics.report_metrics(*, data_source='test', X=None, y=None, scoring=None, scoring_names=None, scoring_kwargs=None, pos_label=None, indicator_favorability=False, flat_index=False)[source]#

Report a set of metrics for our estimator.

Parameters:
data_source{“test”, “train”, “X_y”}, default=”test”

The data source to use.

  • “test” : use the test set provided when creating the report.

  • “train” : use the train set provided when creating the report.

  • “X_y” : use the provided X and y to compute the metric.

Xarray-like of shape (n_samples, n_features), default=None

New data on which to compute the metric. By default, we use the validation set provided when creating the report.

yarray-like of shape (n_samples,), default=None

New target on which to compute the metric. By default, we use the target provided when creating the report.

scoringlist of str, callable, or scorer, default=None

The metrics to report. The possible values in the list are:

  • if a string, either one of the built-in metrics or a scikit-learn scorer name. You can get the possible list of string using report.metrics.help() or sklearn.metrics.get_scorer_names() for the built-in metrics or the scikit-learn scorers, respectively.

  • if a callable, it should take as arguments y_true, y_pred as the two first arguments. Additional arguments can be passed as keyword arguments and will be forwarded with scoring_kwargs.

  • if the callable API is too restrictive (e.g. need to pass same parameter name with different values), you can use scikit-learn scorers as provided by sklearn.metrics.make_scorer().

scoring_nameslist of str, default=None

Used to overwrite the default scoring names in the report. It should be of the same length as the scoring parameter.

scoring_kwargsdict, default=None

The keyword arguments to pass to the scoring functions.

pos_labelint, float, bool or str, default=None

The positive class.

indicator_favorabilitybool, default=False

Whether or not to add an indicator of the favorability of the metric as an extra column in the returned DataFrame.

flat_indexbool, default=False

Whether to flatten the multi-index columns. Flat index will always be lower case, do not include spaces and remove the hash symbol to ease indexing.

Returns:
pd.DataFrame

The statistics for the metrics.

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)
>>> report.metrics.report_metrics(pos_label=1, indicator_favorability=True)
            LogisticRegression Favorability
Metric
Precision              0.98...         (↗︎)
Recall                 0.93...         (↗︎)
ROC AUC                0.99...         (↗︎)
Brier score            0.03...         (↘︎)
>>> # Using scikit-learn metrics
>>> report.metrics.report_metrics(
...     scoring=["f1"], pos_label=1, indicator_favorability=True
... )
                          LogisticRegression Favorability
Metric   Label / Average
F1 Score               1             0.96...          (↗︎)