What’s new in 0.14#

scikit-survival 0.14.0 (2020-10-07)#

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'.

Bug fixes#

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.

Enhancements#

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.