What’s new in 0.22#

scikit-survival 0.22.2 (2023-12-30)#

This release adds support for Python 3.12.

Add section Building Cython Code to contributing guidelines (#379).
Improve the description of the estimate parameter in sksurv.metrics.brier_score() and sksurv.metrics.integrated_brier_score() (#424).

Fix error in sksurv.tree.SurvivalTree.fit() if X has missing values and dtype other than float32 (#412).

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.

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

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.