What’s new in 0.13#

scikit-survival 0.13.1 (2020-07-04)#

This release fixes warnings that were introduced with 0.13.0.

Explicitly pass return_array=True in sksurv.tree.SurvivalTree.predict() to avoid FutureWarning.
Fix error when fitting sksurv.tree.SurvivalTree with non-float dtype for time (#127).
Fix RuntimeWarning: invalid value encountered in true_divide in sksurv.nonparametric.kaplan_meier_estimator().
Fix PendingDeprecationWarning about use of matrix when fitting sksurv.svm.FastSurvivalSVM if optimizer is PRSVM or simple.

The highlights of this release include the addition of sksurv.metrics.brier_score() and sksurv.metrics.integrated_brier_score() and compatibility with scikit-learn 0.23.

predict_survival_function and predict_cumulative_hazard_function of sksurv.ensemble.RandomSurvivalForest and sksurv.tree.SurvivalTree can now return an array of sksurv.functions.StepFunction, similar to sksurv.linear_model.CoxPHSurvivalAnalysis by specifying return_array=False. This will be the default behavior starting with 0.14.0.

Note that this release fixes a bug in estimating inverse probability of censoring weights (IPCW), which will affect all estimators relying on IPCW.

Make build system compatible with PEP-517/518.
Added sksurv.metrics.brier_score() and sksurv.metrics.integrated_brier_score() (#101).
sksurv.functions.StepFunction can now be evaluated at multiple points in a single call.
Update documentation on usage of predict_survival_function and predict_cumulative_hazard_function (#118).
The default value of alpha_min_ratio of sksurv.linear_model.CoxnetSurvivalAnalysis will now depend on the n_samples/n_features ratio. If n_samples > n_features, the default value is 0.0001 If n_samples <= n_features, the default value is 0.01.
Add support for scikit-learn 0.23 (#119).

predict_survival_function and predict_cumulative_hazard_function of sksurv.ensemble.RandomSurvivalForest and sksurv.tree.SurvivalTree will return an array of sksurv.functions.StepFunction in the future (as sksurv.linear_model.CoxPHSurvivalAnalysis does). For the old behavior, use return_array=True.

Fix deprecation of importing joblib via sklearn.
Fix estimation of censoring distribution for tied times with events. When estimating the censoring distribution, by specifying reverse=True when calling sksurv.nonparametric.kaplan_meier_estimator(), we now consider events to occur before censoring. For tied time points with an event, those with an event are not considered at risk anymore and subtracted from the denominator of the Kaplan-Meier estimator. The change affects all functions relying on inverse probability of censoring weights, namely:
Throw an exception when trying to estimate c-index from incomparable data (#117).
Estimators in sksurv.svm will now throw an exception when trying to fit a model to data with incomparable pairs.