ComparisonReport#

class skore.ComparisonReport(reports, *, n_jobs=None)[source]#

Report for comparing reports.

This object can be used to compare several skore.EstimatorReport instances, or several CrossValidationReport instances.

Refer to the Comparison report section of the user guide for more details.

Caution

Reports passed to ComparisonReport are not copied. If you pass a report to ComparisonReport, and then modify the report outside later, it will affect the report stored inside the ComparisonReport as well, which can lead to inconsistent results. For this reason, modifying reports after creation is strongly discouraged.

Parameters:
reportslist of reports or dict

Reports to compare. If a dict, keys will be used to label the estimators; if a list, the labels are computed from the estimator class names. Expects at least two reports to compare, with the same test target.

n_jobsint, default=None

Number of jobs to run in parallel. Training the estimators and computing the scores are parallelized. When accessing some methods of the ComparisonReport, the n_jobs parameter is used to parallelize the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

Attributes:
reports_dict mapping names to reports

The compared reports.

See also

skore.EstimatorReport

Report for a fitted estimator.

skore.CrossValidationReport

Report for the cross-validation of an estimator.

Examples

>>> from sklearn.datasets import make_classification
>>> from skore import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = make_classification(random_state=42)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report.reports_
{'LogisticRegression_1': ..., 'LogisticRegression_2': ...}
>>> report = ComparisonReport(
...     {"model1": estimator_report_1, "model2": estimator_report_2}
... )
>>> report.reports_
{'model1': ..., 'model2': ...}

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import ComparisonReport, CrossValidationReport
>>> X, y = make_classification(random_state=42)
>>> estimator_1 = LogisticRegression()
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> report_1 = CrossValidationReport(estimator_1, X, y)
>>> report_2 = CrossValidationReport(estimator_2, X, y)
>>> report = ComparisonReport([report_1, report_2])
>>> report = ComparisonReport({"model1": report_1, "model2": report_2})
cache_predictions(response_methods='auto', n_jobs=None)[source]#

Cache the predictions for sub-estimators reports.

Parameters:
response_methods{“auto”, “predict”, “predict_proba”, “decision_function”}, default=”auto

The methods to use to compute the predictions.

n_jobsint, default=None

The number of jobs to run in parallel. If None, we use the n_jobs parameter when initializing the report.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = make_classification(random_state=42)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report.cache_predictions()
>>> report._cache
{...}
clear_cache()[source]#

Clear the cache.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = make_classification(random_state=42)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report.cache_predictions()
>>> report.clear_cache()
>>> report._cache
{}
get_predictions(*, data_source, response_method='predict', X=None, pos_label=<DEFAULT>)[source]#

Get predictions from the underlying reports.

This method has the advantage to reload from the cache if the predictions were already computed in a previous call.

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.

response_method{“predict”, “predict_proba”, “decision_function”}, default=”predict”

The response method to use to get the predictions.

Xarray-like of shape (n_samples, n_features), optional

When data_source is “X_y”, the input features on which to compute the response method.

pos_labelint, float, bool, str or None, default=_DEFAULT

The label to consider as the positive class when computing predictions in binary classification cases. By default, the positive class is set to the one provided when creating the report. If None, estimator_.classes_[1] is used as positive label.

When pos_label is equal to estimator_.classes_[0], it will be equivalent to estimator_.predict_proba(X)[:, 0] for response_method="predict_proba" and -estimator_.decision_function(X) for response_method="decision_function".

Returns:
list of np.ndarray of shape (n_samples,) or (n_samples, n_classes) or list of such lists

The predictions for each EstimatorReport or CrossValidationReport.

Raises:
ValueError

If the data source is invalid.

Examples

>>> from sklearn.datasets import make_classification
>>> from skore import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = make_classification(random_state=42)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report.cache_predictions()
>>> predictions = report.get_predictions(data_source="test")
>>> print([split_predictions.shape for split_predictions in predictions])
[(25,), (25,)]
help()[source]#

Display available methods using rich.