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It’s my pleasure to announce the release of scikit-survival 0.24.0.

A highlight of this release the addition of cumulative_incidence_competing_risks() which implements a non-parameteric estimator of the cumulative incidence function in the presence of competing risks. In addition, the release adds support for scikit-learn 1.6, including the support for missing values for ExtraSurvivalTrees.

Analysis of Competing Risks

In classical survival analysis, the focus is on the time until a specific event occurs. If no event is observed during the study period, the time of the event is considered censored. A common assumption is that censoring is non-informative, meaning that censored subjects have a similar prognosis to those who were not censored.

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.

I’m pleased to announce the release of scikit-survival 0.17.2. This release fixes several small issues with packaging scikit-survival and the documentation. For a full list of changes in scikit-survival 0.17.2, please see the release notes.

Most notably, binary wheels are now available for Linux, Windows, and macOS (Intel). This has been possible thanks to the cibuildwheel build tool, which makes it incredible easy to use GitHub Actions for building those wheels for multiple versions of Python. Therefore, you can now use pip without building everything from source by simply running

pip install scikit-survival

As before, pre-built conda packages are available too, by running

 conda install -c sebp scikit-survival

This release adds support for scikit-learn 1.0, which includes support for feature names. If you pass a pandas dataframe to fit, the estimator will set a feature_names_in_ attribute containing the feature names. When a dataframe is passed to predict, it is checked that the column names are consistent with those passed to fit. The example below illustrates this feature.

For a full list of changes in scikit-survival 0.17.0, please see the release notes.

I am proud to announce the release if version 0.16.0 of scikit-survival, The biggest improvement in this release is that you can now change 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 as_concordance_index_ipcw_scorer, as_cumulative_dynamic_auc_scorer, or as_integrated_brier_score_scorer to adjust the score method to your needs. The example below illustrates how to use these in practice.

For a full list of changes in scikit-survival 0.16.0, please see the release notes.

I am proud to announce the release if version 0.15.0 of scikit-survival, which brings support for scikit-learn 0.24 and Python 3.9. Moreover, if you fit a gradient boosting model with loss='coxph', you can now predict the survival and cumulative hazard function using the predict_cumulative_hazard_function and predict_survival_function methods.

The other enhancement is that cumulative_dynamic_auc now supports evaluating time-dependent predictions. For instance, you can now evaluate the predicted time-dependent risk of a RandomSurvivalForest rather than just evaluating the predicted total number of events per instance, which is what RandomSurvivalForest.predict returns.