scikit-survival

This release features a complete overhaul of the documentation. It features a new visual design, and the inclusion of several interactive notebooks in the User Guide.

In addition, it includes important bug fixes. It fixes several bugs in sksurv.linear_model.CoxnetSurvivalAnalysis where predict, predict_survival_function, and predict_cumulative_hazard_function returned wrong values if features of the training data were not centered. Moreover, the score function of sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis and sksurv.ensemble.GradientBoostingSurvivalAnalysis will now correctly compute the concordance index if loss='ipcwls' or loss='squared'.

• sksurv.column.standardize() modified data in-place. Data is now always copied.
• sksurv.column.standardize() works with integer numpy arrays now.
• sksurv.column.standardize() used biased standard deviation for numpy arrays (ddof=0), but unbiased standard deviation for pandas objects (ddof=1). It always uses ddof=1 now. Therefore, the output, if the input is a numpy array, will differ from that of previous versions.
• Fixed sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function() and sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function(), which returned wrong values if features of training data were not already centered. This adds an offset_ attribute that accounts for non-centered data and is added to the predicted risk score. Therefore, the outputs of predict, predict_survival_function, and predict_cumulative_hazard_function will be different to previous versions for non-centered data (#139).
• Rescale coefficients of sksurv.linear_model.CoxnetSurvivalAnalysis if normalize=True.
• Fix score function of sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis and sksurv.ensemble.GradientBoostingSurvivalAnalysis if loss='ipcwls' or loss='squared' is used. Previously, it returned 1.0 - true_cindex.

• Add sksurv.show_versions() that prints the version of all dependencies.
• Add support for pandas 1.1
• Include interactive notebooks in documentation on readthedocs.
• Add user guide on penalized Cox models.
• Add user guide on gradient boosted models.