crepes 0.8.0

v0.8.0 (31/03/2025)

Features

• Semi-online inductive conformal classifiers, regressors and predictive systems, which iteratively update the calibration set after making each prediction, have been incorporated. For WrapClassifier, the methods predict_p, predict_set and evaluate, now include the argument online together with the argument warm_start, where the former enables online calibration (disabled by default) and the latter enables extending the original calibration set during online calibration (enabled by default). Similarly, for WrapRegressor, the methods predict_p, predict_int, predict_percentiles, predict_cpds and evaluate include the arguments online and warm_start. Semi-online conformal predictors are also enabled by the methods predict_p_online (for ConformalClassifier, ConformalRegressor, and ConformalPredictiveSystem), predict_set_online (for ConformalClassifier), predict_int_online (for ConformalRegressor and ConformalPredictiveSystem), predict_percentiles_online and predict_cpds_online (for ConformalPredictiveSystem). Moreover, the evaluate method for all three types of conformal predictor now includes an additional argument online, which if set to true enables online calibration.

• The method predict_p for generating (smoothed or non-smoothed) p-values using conformal regressors has been added.

• New methods for conformal predictive systems have been added; predict_p for obtaining p-values, predict_int for obtaining prediction intervals, predict_percentiles for obtaining percentiles, and predict_cpds for obtaining conformal predictive distributions. These outputs can alternatively (as before) be generated by the methods predict (for ConformalPredictiveSystem) and predict_cps (for WrapRegressor).

• Both smoothed and non-smoothed p-values may now be output for conformal predictive systems; smoothing is the default but can be disabled by setting smoothing=False for the methods predict_p and predict.

• The evaluate method of ConformalClassifier, ConformalRegressor, and ConformalPredictiveSystem, as well as of WrapClassifier and WrapRegressor, now includes the metric ks_test, which provides the p-value for the Kolmogorov-Smirnov test of uniformity of predicted p-values. Thanks to @egonmedhatten for the suggestion!

Fixes

• Fitted objects now contain the attribute fitted_ to allow for proper handling in scikit-learn pipelines. Thanks to @lukethomrichardson for suggesting the fix.