Scikit-survival

This release adds support for scikit-learn 0.22, thereby dropping support for older versions. Moreover, the regularization strength of the ridge penalty in [sksurv.linear_model.CoxPHSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.linear_model.CoxPHSurvivalAnalysis.html#sksurv.linear_model.CoxPHSurvivalAnalysis) can now be set per feature. If you want one or more features to enter the model unpenalized, set the corresponding penalty weights to zero. Finally, [sklearn.pipeline.Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#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.

Deprecations

- Add scikit-learn's deprecation of `presort` in [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree) and [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis).
- Add warning that default `alpha_min_ratio` in [sksurv.linear_model.CoxnetSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.linear_model.CoxnetSurvivalAnalysis.html#sksurv.linear_model.CoxnetSurvivalAnalysis) will depend on the ratio of the number of samples to the number of features in the future (41).

Enhancements

- Add references to API doc of [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis) (91).
- Add support for pandas 1.0 (100).
- Add `ccp_alpha` parameter for [Minimal Cost-Complexity Pruning](https://scikit-learn.org/stable/modules/tree.html#minimal-cost-complexity-pruning) to [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis).
- Patch [sklearn.pipeline.Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline) to add support for `predict_cumulative_hazard_function` and `predict_survival_function` if the underlying estimator supports it.
- Allow per-feature regularization for [sksurv.linear_model.CoxPHSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.linear_model.CoxPHSurvivalAnalysis.html#sksurv.linear_model.CoxPHSurvivalAnalysis) (102).
- Clarify API docs of [sksurv.metrics.concordance_index_censored](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.metrics.concordance_index_censored.html#sksurv.metrics.concordance_index_censored) (96).

This release adds [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree) and [sksurv.ensemble.RandomSurvivalForest](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.ensemble.RandomSurvivalForest.html#sksurv.ensemble.RandomSurvivalForest),
which are based on the log-rank split criterion. It also adds the OSQP solver as option to [sksurv.svm.MinlipSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.svm.MinlipSurvivalAnalysis.html#sksurv.svm.MinlipSurvivalAnalysis) and [sksurv.svm.HingeLossSurvivalSVM](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.svm.HingeLossSurvivalSVM.html#sksurv.svm.HingeLossSurvivalSVM), which will replace the now deprecated `cvxpy` and `cvxopt` options in a future release.

This release removes support for sklearn 0.20 and requires sklearn 0.21.

Deprecations

- The `cvxpy` and `cvxopt` options for `solver` in [sksurv.svm.MinlipSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.svm.MinlipSurvivalAnalysis.html#sksurv.svm.MinlipSurvivalAnalysis) and
[sksurv.svm.HingeLossSurvivalSVM](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.svm.HingeLossSurvivalSVM.html#sksurv.svm.HingeLossSurvivalSVM) are deprecated and will be removed in a future version. Choosing `osqp` is the preferred option now.

Enhancements

- Add support for pandas 0.25.
- Add OSQP solver option to [sksurv.svm.MinlipSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.svm.MinlipSurvivalAnalysis.html#sksurv.svm.MinlipSurvivalAnalysis), and
[sksurv.svm.HingeLossSurvivalSVM](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.svm.HingeLossSurvivalSVM.html#sksurv.svm.HingeLossSurvivalSVM) which has no additional dependencies.
- Fix issue when using cvxpy 1.0.16 or later.
- Explicitly specify utf-8 encoding when reading README.rst (89).
- Add [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree) and [sksurv.ensemble.RandomSurvivalForest](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.ensemble.RandomSurvivalForest.html#sksurv.ensemble.RandomSurvivalForest) (90).

Bug fixes

- Exclude Cython-generated files from source distribution because
they are not forward compatible.

This release adds the *ties* argument to [sksurv.linear_model.CoxPHSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.linear_model.CoxPHSurvivalAnalysis.html#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()](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.compare.compare_survival.html#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

- Re-add *baseline_survival_* and *cum_baseline_hazard_* attributes to [sksurv.linear_model.CoxPHSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/latest/generated/sksurv.linear_model.CoxPHSurvivalAnalysis.html#sksurv.linear_model.CoxPHSurvivalAnalysis) (76).

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).

Enhancements

- Add `sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function`
and `sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function`
(46).
- Add `sksurv.nonparametric.SurvivalFunctionEstimator`
and `sksurv.nonparametric.CensoringDistributionEstimator` that
wrap `sksurv.nonparametric.kaplan_meier_estimator` and provide
a `predict_proba` method for evaluating the estimated function on
test data.
- Implement censoring-adjusted C-statistic proposed by Uno et al. (2011)
in `sksurv.metrics.concordance_index_ipcw`.
- Add estimator of cumulative/dynamic AUC of Uno et al. (2007)
in `sksurv.metrics.cumulative_dynamic_auc`.
- Add flchain dataset (see `sksurv.datasets.load_flchain`).

Bug fixes

- The `tied_time` return value of `sksurv.metrics.concordance_index_censored`
now correctly reflects the number of comparable pairs that share the same time
and that are used in computing the concordance index.
- Fix a bug in `sksurv.metrics.concordance_index_censored` where a
pair with risk estimates within tolerance was counted both as
concordant and tied.

This release adds support for Python 3.7 and sklearn 0.20.

Changes:
* Add support for sklearn 0.20 (48).
* Migrate to py.test (50).
* Explicitly request ECOS solver for `sksurv.svm.MinlipSurvivalAnalysis` and `sksurv.svm.HingeLossSurvivalSVM`.
* Add support for Python 3.7 (49).
* Add support for cvxpy >=1.0.
* Add support for numpy 1.15.