scikit-survival

This is a major release bringing new features and performance improvements.

• sksurv.nonparametric.kaplan_meier_estimator() can estimate pointwise confidence intervals by specifying the conf_type parameter.
• sksurv.ensemble.GradientBoostingSurvivalAnalysis supports early-stopping via the monitor parameter of sksurv.ensemble.GradientBoostingSurvivalAnalysis.fit().
• sksurv.metrics.concordance_index_censored() has a significantly reduced memory footprint. Memory usage now scales linear, instead of quadratic, in the number of samples.
• Fitting of sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, or sksurv.ensemble.ExtraSurvivalTrees is about 3x faster.
• Finally, the release adds support for Python 3.11 and pandas 2.0.

• Fix bug where times passed to sksurv.metrics.brier_score() was downcast, resulting in a loss of precision that may lead to duplicate time points (#349).
• Fix inconsistent behavior of evaluating functions returned by predict_cumulative_hazard_function or predict_survival_function (#375).

• sksurv.nonparametric.kaplan_meier_estimator() and sksurv.nonparametric.CensoringDistributionEstimator support returning confidence intervals by specifying the conf_type parameter (#348).
• Configure package via pyproject.toml (#347).
• Add support for Python 3.11 (#350).
• Add support for early-stopping to sksurv.ensemble.GradientBoostingSurvivalAnalysis (#354).
• Do not use deprecated pkg_resources API (#353).
• Significantly reduce memory usage of sksurv.metrics.concordance_index_censored() (#362).
• Set criterion attribute in sksurv.tree.SurvivalTree such that sklearn.tree.plot_tree() can be used (#366).
• Significantly improve speed to fit a sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, or sksurv.ensemble.ExtraSurvivalTrees (#371).
• Expose _predict_risk_score attribute in sklearn.pipeline.Pipeline if the final estimator of the pipeline has such property (#374).
• Add support for pandas 2.0 (#373).

• Fix wrong number of selected features in the guide Introduction to Survival Analysis (#345).
• Fix broken links with nbsphinx 0.9.2 (#367).

• The attribute event_times_ of estimators has been replaced by unique_times_ to clarify that these are all the unique times points, not just the once where an event occurred (#371).
• Functions returned by predict_cumulative_hazard_function and predict_survival_function of sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, and sksurv.ensemble.ExtraSurvivalTrees are over all unique time points passed as training data, instead of all unique time points where events occurred (#371).
• Evaluating a function returned by predict_cumulative_hazard_function or predict_survival_function will no longer raise an exception if the specified time point is smaller than the smallest time point observed during training. Instead, the value at StepFunction.x[0] will be returned (#375).

• @dor132 made their first contribution in https://github.com/sebp/scikit-survival/pull/345
• @cpoerschke made their first contribution in https://github.com/sebp/scikit-survival/pull/358

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.20.0...v0.21.0