API

This page lists all the public functions and classes of the skore package.

Warning

This code is still in development. The API is subject to change.

Project

These functions and classes are meant for managing a Project.

open([project_path, create, overwrite])	Open a project given a project name or path.
Project(item_repository, view_repository)	A collection of items arranged in views and stored in a storage.
Project.put(key, value, *[, note, display_as])	Add a key-value pair to the Project.
Project.get(key, *[, version, metadata])	Get the value associated to key from the Project.

Get assistance when developing ML/DS projects

These functions and classes enhance scikit-learn’s ones.

train_test_split(*arrays[, X, y, test_size, ...])

Perform train-test-split of data.

Report for a single estimator

The class EstimatorReport provides a report allowing to inspect and evaluate a scikit-learn estimator in an interactive way. The functionalities of the report are accessible through accessors.

EstimatorReport(estimator, *[, fit, ...])

Report for a fitted estimator.

EstimatorReport.help

Display available methods using rich.

EstimatorReport.metrics

Accessor for metrics-related operations.

Metrics

The metrics accessor helps you to evaluate the statistical performance of your estimator. In addition, we provide a sub-accessor plot, to get the common performance metric representations.

EstimatorReport.metrics.help	Display available methods using rich.
EstimatorReport.metrics.report_metrics	Report a set of metrics for our estimator.
EstimatorReport.metrics.custom_metric	Compute a custom metric.
EstimatorReport.metrics.accuracy	Compute the accuracy score.
EstimatorReport.metrics.brier_score	Compute the Brier score.
EstimatorReport.metrics.log_loss	Compute the log loss.
EstimatorReport.metrics.precision	Compute the precision score.
EstimatorReport.metrics.r2	Compute the R² score.
EstimatorReport.metrics.recall	Compute the recall score.
EstimatorReport.metrics.rmse	Compute the root mean squared error.
EstimatorReport.metrics.roc_auc	Compute the ROC AUC score.
EstimatorReport.metrics.plot.help	Display available methods using rich.
EstimatorReport.metrics.plot.precision_recall	Plot the precision-recall curve.
EstimatorReport.metrics.plot.prediction_error	Plot the prediction error of a regression model.
EstimatorReport.metrics.plot.roc	Plot the ROC curve.

Cross-validation report for an estimator

The class CrossValidationReport provides a report allowing to inspect and evaluate a scikit-learn estimator through cross-validation in an interactive way. The functionalities of the report are accessible through accessors.

CrossValidationReport(estimator, X[, y, ...])

Report for cross-validation results.

CrossValidationReport.help

Display available methods using rich.

CrossValidationReport.metrics

Accessor for metrics-related operations.

Metrics

The metrics accessor helps you to evaluate the statistical performance of your estimator during a cross-validation. In addition, we provide a sub-accessor plot, to get the common performance metric representations.

CrossValidationReport.metrics.help	Display available methods using rich.
CrossValidationReport.metrics.report_metrics	Report a set of metrics for our estimator.
CrossValidationReport.metrics.custom_metric	Compute a custom metric.
CrossValidationReport.metrics.accuracy	Compute the accuracy score.
CrossValidationReport.metrics.brier_score	Compute the Brier score.
CrossValidationReport.metrics.log_loss	Compute the log loss.
CrossValidationReport.metrics.precision	Compute the precision score.
CrossValidationReport.metrics.r2	Compute the R² score.
CrossValidationReport.metrics.recall	Compute the recall score.
CrossValidationReport.metrics.rmse	Compute the root mean squared error.
CrossValidationReport.metrics.roc_auc	Compute the ROC AUC score.
CrossValidationReport.metrics.plot.help	Display available methods using rich.
CrossValidationReport.metrics.plot.precision_recall	Plot the precision-recall curve.
CrossValidationReport.metrics.plot.prediction_error	Plot the prediction error of a regression model.
CrossValidationReport.metrics.plot.roc	Plot the ROC curve.

Deprecated

These functions and classes are deprecated.

CrossValidationReporter(*args, **kwargs)

Evaluate estimator by cross-validation and output UI-friendly object.