What’s new in 0.16

scikit-survival 0.16.0 (2021-10-30)

This release adds support for changing the evaluation metric that is used in estimators’ score method. This is particular useful for hyper-parameter optimization using scikit-learn’s GridSearchCV. You can now use sksurv.metrics.as_concordance_index_ipcw_scorer, sksurv.metrics.as_cumulative_dynamic_auc_scorer, or sksurv.metrics.as_integrated_brier_score_scorer to adjust the score method to your needs. A detailed example is available in the User Guide.

Moreover, this release adds sksurv.ensemble.ExtraSurvivalTrees to fit an ensemble of randomized survival trees, and improves the speed of sksurv.compare.compare_survival() significantly. The documentation has been extended by a section on the time-dependent Brier score.

Bug fixes

• Columns are dropped in sksurv.column.encode_categorical() despite allow_drop=False (#199).

• Ensure sksurv.column.categorical_to_numeric() always returns series with int64 dtype.

Enhancements

• Add sksurv.ensemble.ExtraSurvivalTrees ensemble (#195).

• Faster speed for sksurv.compare.compare_survival() (#215).

• Add wrapper classes sksurv.metrics.as_concordance_index_ipcw_scorer, sksurv.metrics.as_cumulative_dynamic_auc_scorer, and sksurv.metrics.as_integrated_brier_score_scorer to override the default score method of estimators (#192).

• Remove use of deprecated numpy dtypes.

• Remove use of inplace in pandas’ set_categories.

Documentation

• Remove comments and code suggesting log-transforming times prior to training Survival SVM (#203).

• Add documentation for max_samples parameter to sksurv.ensemble.ExtraSurvivalTrees and sksurv.ensemble.RandomSurvivalForest (#217).

• Add section on time-dependent Brier score (#220).

• Add section on using alternative metrics for hyper-parameter optimization.