Today, I released version 0.13.0 of scikit-survival. Most notably, this release adds sksurv.metrics.brier_score and sksurv.metrics.integrated_brier_score, an updated PEP 517/518 compatible build system, and support for scikit-learn 0.23.

For a full list of changes in scikit-survival 0.13.0, 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.

A while back, I posted the Survival Analysis for Deep Learning tutorial. This tutorial was written for TensorFlow 1 using the tf.estimators API. The changes between version 1 and the current TensorFlow 2 are quite significant, which is why the code does not run when using a recent TensorFlow version. Therefore, I created a new version of the tutorial that is compatible with TensorFlow 2. The text is basically identical, but the training and evaluation procedure changed.

The complete notebook is available on GitHub, or you can run it directly using Google Colaboratory.

Version 0.12 of scikit-survival adds support for scikit-learn 0.22 and Python 3.8 and comes with two noticeable improvements:

1. sklearn.pipeline.Pipeline will now be automatically patched to add support for predict_cumulative_hazard_function and predict_survival_function if the underlying estimator supports it (see first example ).
2. The regularization strength of the ridge penalty in sksurv.linear_model.CoxPHSurvivalAnalysis can now be set per feature (see second example ).

For a full list of changes in scikit-survival 0.12, please see the release notes.

Today, I released a new version of scikit-survival which includes an implementation of Random Survival Forests. As it’s popular counterparts for classification and regression, a Random Survival Forest is an ensemble of tree-based learners. A Random Survival Forest ensures that individual trees are de-correlated by 1) building each tree on a different bootstrap sample of the original training data, and 2) at each node, only evaluate the split criterion for a randomly selected subset of features and thresholds. Predictions are formed by aggregating predictions of individual trees in the ensemble.

For a full list of changes in scikit-survival 0.11, please see the release notes.

This release of scikit-survival adds two features that are standard in most software for survival analysis, but were missing so far:

1. CoxPHSurvivalAnalysis now has a ties parameter that allows you to choose between Breslow’s and Efron’s likelihood for handling tied event times. Previously, only Breslow’s likelihood was implemented and it remains the default. If you have many tied event times in your data, you can now select Efron’s likelihood with ties="efron" to get better estimates of the model’s coefficients.
2. A compare_survival function has been added. It can be used to assess whether survival functions across 2 or more groups differ.