Release Notes

scikit-survival 0.10 (2019-09-02)

This release adds the ties argument to sksurv.linear_model.CoxPHSurvivalAnalysis to choose between Breslow’s and Efron’s likelihood in the presence of tied event times. Moreover, sksurv.compare.compare_survival() has been added, which implements the log-rank hypothesis test for comparing the survival function of 2 or more groups.

Enhancements

  • Update API doc of predict function of boosting estimators (#75).
  • Clarify documentation for GradientBoostingSurvivalAnalysis (#78).
  • Implement Efron’s likelihood for handling tied event times.
  • Implement log-rank test for comparing survival curves.
  • Add support for scipy 1.3.1 (#66).

Bug fixes

scikit-survival 0.9 (2019-07-26)

This release adds support for sklearn 0.21 and pandas 0.24.

Enhancements

  • Add reference to IPCRidge (#65).
  • Use scipy.special.comb instead of deprecated scipy.misc.comb.
  • Add support for pandas 0.24 and drop support for 0.20.
  • Add support for scikit-learn 0.21 and drop support for 0.20 (#71).
  • Explain use of intercept in ComponentwiseGradientBoostingSurvivalAnalysis (#68)
  • Bump Eigen to 3.3.7.

Bug fixes

  • Disallow scipy 1.3.0 due to scipy regression (#66).

scikit-survival 0.8 (2019-05-01)

Enhancements

Bug fixes

scikit-survival 0.7 (2019-02-27)

This release adds support for Python 3.7 and sklearn 0.20.

Changes:

scikit-survival 0.6 (2018-10-07)

This release adds support for numpy 1.14 and pandas up to 0.23. In addition, the new class sksurv.util.Surv makes it easier to construct a structured array from numpy arrays, lists, or a pandas data frame.

Changes:

scikit-survival 0.5 (2017-12-09)

This release adds support for scikit-learn 0.19 and pandas 0.21. In turn, support for older versions is dropped, namely Python 3.4, scikit-learn 0.18, and pandas 0.18.

scikit-survival 0.4 (2017-10-28)

This release adds sksurv.linear_model.CoxnetSurvivalAnalysis, which implements an efficient algorithm to fit Cox’s proportional hazards model with LASSO, ridge, and elastic net penalty. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.

scikit-survival 0.3 (2017-08-01)

This release adds sksurv.linear_model.CoxPHSurvivalAnalysis.predict_survival_function() and sksurv.linear_model.CoxPHSurvivalAnalysis.predict_cumulative_hazard_function(), which return the survival function and cumulative hazard function using Breslow’s estimator. Moreover, it fixes a build error on Windows (gh #3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.

scikit-survival 0.2 (2017-05-29)

This release adds support for Python 3.6, and pandas 0.19 and 0.20.

scikit-survival 0.1 (2016-12-29)

This is the initial release of scikit-survival. It combines the implementation of survival support vector machines with the code used in the Prostate Cancer DREAM challenge.