ComparisonReport#

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

Report for comparison of instances of skore.EstimatorReport.

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 EstimatorReport instances or dict

Estimator reports to compare.

  • If reports is a list, the class name of each estimator is used.

  • If reports is a dict, it is expected to have estimator names as keys and EstimatorReport instances as values. If the keys are not strings, they will be converted to strings.

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:
estimator_reports_list of EstimatorReport

The compared estimator reports.

report_names_list of str

The names of the compared estimator 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 sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = make_classification(random_state=42)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(
...     estimator_1,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test
... )
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(
...     estimator_2,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test
... )
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report = ComparisonReport(
...     {"model1": estimator_report_1, "model2": estimator_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 sklearn.model_selection import train_test_split
>>> from skore import ComparisonReport
>>> X, y = make_classification(random_state=42)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(
...     estimator_1,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test
... )
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(
...     estimator_2,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test
... )
>>> 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 sklearn.model_selection import train_test_split
>>> from skore import ComparisonReport
>>> X, y = make_classification(random_state=42)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(
...     estimator_1,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test
... )
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(
...     estimator_2,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test
... )
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report.cache_predictions()
>>> report.clear_cache()
>>> report._cache
{}
get_predictions(*, data_source, response_method, pos_label=None)[source]#

Get estimator’s predictions.

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”}

The response method to use.

pos_labelint, float, bool or str, default=None

The positive class when it comes to binary classification. When response_method="predict_proba", it will select the column corresponding to the positive class. When response_method="decision_function", it will negate the decision function if pos_label is different from estimator.classes_[1].

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

The predictions for each cross-validation split.

Raises:
ValueError

If the data source is invalid.

Examples

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

Display available methods using rich.

metrics[source]#

alias of _MetricsAccessor