What’s new in 0.12#
scikit-survival 0.12 (2020-04-15)#
This release adds support for scikit-learn 0.22, thereby dropping support for
older versions. Moreover, the regularization strength of the ridge penalty
in sksurv.linear_model.CoxPHSurvivalAnalysis can now be set per
feature. If you want one or more features to enter the model unpenalized,
set the corresponding penalty weights to zero.
Finally, sklearn.pipeline.Pipeline will now be automatically patched
to add support for predict_cumulative_hazard_function and predict_survival_function
if the underlying estimator supports it.
Deprecations#
Add scikit-learn’s deprecation of presort in
sksurv.tree.SurvivalTreeandsksurv.ensemble.GradientBoostingSurvivalAnalysis.Add warning that default alpha_min_ratio in
sksurv.linear_model.CoxnetSurvivalAnalysiswill depend on the ratio of the number of samples to the number of features in the future (#41).
Enhancements#
Add references to API doc of
sksurv.ensemble.GradientBoostingSurvivalAnalysis(#91).Add support for pandas 1.0 (#100).
Add ccp_alpha parameter for Minimal Cost-Complexity Pruning to
sksurv.ensemble.GradientBoostingSurvivalAnalysis.Patch
sklearn.pipeline.Pipelineto add support for predict_cumulative_hazard_function and predict_survival_function if the underlying estimator supports it.Allow per-feature regularization for
sksurv.linear_model.CoxPHSurvivalAnalysis(#102).Clarify API docs of
sksurv.metrics.concordance_index_censored()(#96).