I am pleased to announce the release of scikit-survival 0.23.0.

This release adds support for scikit-learn 1.4 and 1.5, which includes missing value support for RandomSurvivalForest. For more details on missing values support, see the section in the release announcement for 0.23.0.

Moreover, this release fixes critical bugs. When fitting SurvivalTree, the sample_weight is now correctly considered when computing the log-rank statistic for each split. This change also affects RandomSurvivalForest and ExtraSurvivalTrees which pass sample_weight to the individual trees in the ensemble. Therefore, the outputs produced by SurvivalTree, RandomSurvivalForest, and ExtraSurvivalTrees will differ from previous releases.

I am pleased to announce the release of scikit-survival 0.22.0. The highlights for this release include

• Compatibility with scikit-learn 1.3.
• Missing value support for SurvivalTree.
• A reduced memory mode for RandomSurvivalForest, ExtraSurvivalTrees, and SurvivalTree.
• Support for predict_cumulative_hazard_function() and predict_survival_function() in Stacking.

Today marks the release of scikit-survival 0.21.0. This release features some exciting new features and significant performance improvements:

• Pointwise confidence intervals for the Kaplan-Meier estimator.
• Early stopping in GradientBoostingSurvivalAnalysis.
• Improved performance of fitting SurvivalTree and RandomSurvivalForest.
• Reduced memory footprint of concordance_index_censored.

I’m pleased to announce the release of scikit-survival 0.18.0, which adds support for scikit-learn 1.1.

In addition, this release adds the return_array argument to all models providing predict_survival_function and predict_cumulative_hazard_function. That means you can now choose, whether you want to have the survival (cumulative hazard function) automatically evaluated at the unique event times. This is particular useful for plotting. Previously, you would have to evaluate each survival function before plotting:

I recently wanted to create a development container for VS Code to develop applications using SYCL based on the CUDA backend of the oneAPI DPC++ (Data Parallel C++) compiler. As I’m running Fedora, it seemed natural to use Podman’s rootless containers instead of Docker for this. This turned out to be more challenging than expected, so I’m going to summarize my setup in this post. I’m using Fedora Linux 36 with Podman version 4.1.0.