Today marks the release of version 0.14.0 of scikit-survival. The biggest change in this release is actually not in the code, but in the documentation. This release features a complete overhaul of the documentation. Most importantly, the documentation has a more modern feel to it, thanks to the visually pleasing pydata Sphinx theme, which also powers pandas.
Moreover, the documentation now contains a User Guide section that bundles several topics surrounding the use of scikit-survival. Some of these were available as separate Jupyter notebooks previously, such as the guide on Evaluating Survival Models. There are two new guides: The first one is on penalized Cox models. It provides a hands-on introduction to Cox’s proportional hazards model with $\ell_2$ (Ridge) and $\ell_1$ (LASSO) penalty. The second guide, is on Gradient Boosted Models and covers how gradient boosting can be used to obtain a non-linear proportional hazards model or a non-linear accelerated failure time model by using regression tree base learners. The second part of this guide covers a variant of gradient boosting that is most suitable for high-dimensional data and is based on component-wise least squares base learners.
In addition to the vastly improved documentation, this release includes important bug fixes. It fixes several bugs in CoxnetSurvivalAnalysis, where
predict_cumulative_hazard_function returned wrong values if features of the training data were not centered. Moreover, the score function of ComponentwiseGradientBoostingSurvivalAnalysis and GradientBoostingSurvivalAnalysis will now correctly compute the concordance index if
For a full list of changes in scikit-survival 0.14.0, please see the release notes.
Pre-built conda packages are available for Linux, macOS, and Windows via
conda install -c sebp scikit-survival
Alternatively, scikit-survival can be installed from source following these instructions.