v0.22.0

This release adds support for scikit-learn 1.3, which includes missing value support for sksurv.tree.SurvivalTree. Support for previous versions of scikit-learn has been dropped.

Moreover, a low_memory option has been added to sksurv.ensemble.RandomSurvivalForest, sksurv.ensemble.ExtraSurvivalTrees, and sksurv.tree.SurvivalTree which reduces the memory footprint of calling predict, but disables the use of predict_cumulative_hazard_function and predict_survival_function.

Bug fixes

• Fix issue where an estimator could be fit to data containing negative event times (#410).

Enhancements

• Expand test_stacking.py coverage w.r.t. predict_log_proba (#380).
• Add low_memory option to sksurv.ensemble.RandomSurvivalForest, sksurv.ensemble.ExtraSurvivalTrees, and sksurv.tree.SurvivalTree. If set, predict computations use less memory, but predict_cumulative_hazard_function and predict_survival_function are not implemented (#369).
• Allow calling sksurv.meta.Stacking.predict_cumulative_hazard_function() and sksurv.meta.Stacking.predict_survival_function() if the meta estimator supports it (#388).
• Add support for missing values in sksurv.tree.SurvivalTree based on missing value support in scikit-learn 1.3 (#405).
• Update bundled Eigen to 3.4.0.

Documentation

• Add sksurv.meta.Stacking.unique_times_ to API docs.
• Upgrade to Sphinx 6.2.1 and pydata_sphinx_theme 0.13.3 (#390).

Backwards incompatible changes

• The loss_ attribute of sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis and sksurv.ensemble.GradientBoostingSurvivalAnalysis has been removed (#402).

• Support for max_features='auto' in sksurv.ensemble.GradientBoostingSurvivalAnalysis and sksurv.tree.SurvivalTree has been removed (#402).

Full Changelog: v0.21.0...v0.22.0