scikit-survival

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.SurvivalTree and sksurv.ensemble.GradientBoostingSurvivalAnalysis. Add warning that default alpha_min_ratio in sksurv.linear_model.CoxnetSurvivalAnalysis will 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.Pipeline to 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 :func:sksurv.metrics.concordance_index_censored (#96)