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
{}
create_estimator_report(*, name, X_test=None, y_test=None)[source]#

Create an estimator report from one of the reports in the comparison.

This method creates a new EstimatorReport with the same estimator and the same data as the chosen report. It is useful to evaluate and deploy a model that was deemed optimal during the comparison. Provide a held out test set to properly evaluate the performance of the model.

Parameters:
namestr

The name of the estimator to create a report for.

X_test{array-like, sparse matrix} of shape (n_samples, n_features) or None

Testing data. It should have the same structure as the training data.

y_testarray-like of shape (n_samples,) or (n_samples, n_outputs) or None

Testing target.

Returns:
reportEstimatorReport

The estimator report.

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), default=None

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 report help using rich or HTML.