v0.22.2

This release adds support for Python 3.12.

Bug fixes

• Fix invalid escape sequence in Introduction of user guide.

Enhancements

• Mark Cython functions as noexcept (#418).
• Add support for Python 3.12 (#422).
• Do not use deprecated is_categorical_dtype() of Pandas API.

Documentation

• Add section Building Cython Code to contributing guidelines (#379).
• Improve the description of the estimate parameter in sksurv.metrics.brier_score() and sksurv.metrics.integrated_brier_score() (#424).

Full Changelog: v0.22.1...v0.22.2

v0.22.1

Bug fixes

• Fix error in sksurv.tree.SurvivalTree.fit() if X has missing values and dtype other than float32 (#412).

v0.22.0

This release adds support for scikit-learn 1.3, which includes missing value support for sksurv.tree.SurvivalTree. Support for previous versions of scikit-learn has been dropped.

Moreover, a low_memory option has been added to sksurv.ensemble.RandomSurvivalForest, sksurv.ensemble.ExtraSurvivalTrees, and sksurv.tree.SurvivalTree which reduces the memory footprint of calling predict, but disables the use of predict_cumulative_hazard_function and predict_survival_function.

Bug fixes

• Fix issue where an estimator could be fit to data containing negative event times (#410).

Enhancements

• Expand test_stacking.py coverage w.r.t. predict_log_proba (#380).
• Add low_memory option to sksurv.ensemble.RandomSurvivalForest, sksurv.ensemble.ExtraSurvivalTrees, and sksurv.tree.SurvivalTree. If set, predict computations use less memory, but predict_cumulative_hazard_function and predict_survival_function are not implemented (#369).
• Allow calling sksurv.meta.Stacking.predict_cumulative_hazard_function() and sksurv.meta.Stacking.predict_survival_function() if the meta estimator supports it (#388).
• Add support for missing values in sksurv.tree.SurvivalTree based on missing value support in scikit-learn 1.3 (#405).
• Update bundled Eigen to 3.4.0.

Documentation

• Add sksurv.meta.Stacking.unique_times_ to API docs.
• Upgrade to Sphinx 6.2.1 and pydata_sphinx_theme 0.13.3 (#390).

Backwards incompatible changes

• The loss_ attribute of sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis and sksurv.ensemble.GradientBoostingSurvivalAnalysis has been removed (#402).

• Support for max_features='auto' in sksurv.ensemble.GradientBoostingSurvivalAnalysis and sksurv.tree.SurvivalTree has been removed (#402).

Full Changelog: v0.21.0...v0.22.0

v0.21.0

This is a major release bringing new features and performance improvements.

• sksurv.nonparametric.kaplan_meier_estimator() can estimate pointwise confidence intervals by specifying the conf_type parameter.
• sksurv.ensemble.GradientBoostingSurvivalAnalysis supports early-stopping via the monitor parameter of sksurv.ensemble.GradientBoostingSurvivalAnalysis.fit().
• sksurv.metrics.concordance_index_censored() has a significantly reduced memory footprint. Memory usage now scales linear, instead of quadratic, in the number of samples.
• Fitting of sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, or sksurv.ensemble.ExtraSurvivalTrees is about 3x faster.
• Finally, the release adds support for Python 3.11 and pandas 2.0.

Bug fixes

• Fix bug where times passed to sksurv.metrics.brier_score() was downcast, resulting in a loss of precision that may lead to duplicate time points (#349).
• Fix inconsistent behavior of evaluating functions returned by predict_cumulative_hazard_function or predict_survival_function (#375).

Enhancements

• sksurv.nonparametric.kaplan_meier_estimator() and sksurv.nonparametric.CensoringDistributionEstimator support returning confidence intervals by specifying the conf_type parameter (#348).
• Configure package via pyproject.toml (#347).
• Add support for Python 3.11 (#350).
• Add support for early-stopping to sksurv.ensemble.GradientBoostingSurvivalAnalysis (#354).
• Do not use deprecated pkg_resources API (#353).
• Significantly reduce memory usage of sksurv.metrics.concordance_index_censored() (#362).
• Set criterion attribute in sksurv.tree.SurvivalTree such that sklearn.tree.plot_tree() can be used (#366).
• Significantly improve speed to fit a sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, or sksurv.ensemble.ExtraSurvivalTrees (#371).
• Expose _predict_risk_score attribute in sklearn.pipeline.Pipeline if the final estimator of the pipeline has such property (#374).
• Add support for pandas 2.0 (#373).

Documentation

• Fix wrong number of selected features in the guide Introduction to Survival Analysis (#345).
• Fix broken links with nbsphinx 0.9.2 (#367).

Backwards incompatible changes

• The attribute event_times_ of estimators has been replaced by unique_times_ to clarify that these are all the unique times points, not just the once where an event occurred (#371).
• Functions returned by predict_cumulative_hazard_function and predict_survival_function of sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, and sksurv.ensemble.ExtraSurvivalTrees are over all unique time points passed as training data, instead of all unique time points where events occurred (#371).
• Evaluating a function returned by predict_cumulative_hazard_function or predict_survival_function will no longer raise an exception if the specified time point is smaller than the smallest time point observed during training. Instead, the value at StepFunction.x[0] will be returned (#375).

New Contributors

• @dor132 made their first contribution in #345
• @cpoerschke made their first contribution in #358

Full Changelog: v0.20.0...v0.21.0

v0.20.0

This release adds support for scikit-learn 1.2 and drops support for previous versions.

Enhancements

• Raise more informative error messages when a parameter does not have a valid type/value (see sklearn#23462).
• Add positive and random_state parameters to sksurv.linear_model.IPCRidge.

Documentation

• Update API docs based on scikit-learn 1.2 (where applicable).

Backwards incompatible changes

• To align with the scikit-learn API, many parameters of estimators must be provided with their names, as keyword arguments, instead of positional arguments.

• Remove deprecated normalize parameter from sksurv.linear_model.IPCRidge.

• Remove deprecated X_idx_sorted argument from sksurv.tree.SurvivalTree.fit().

• Setting kernel="polynomial" in sksurv.svm.FastKernelSurvivalSVM, sksurv.svm.HingeLossSurvivalSVM, and sksurv.svm.MinlipSurvivalAnalysis has been replaced with kernel="poly".

Full Changelog: v0.19.0.post1...v0.20.0

v0.19.0.post1

This release raises the install requirement of scikit-learn to 1.1.2 to avoid binary incompatibility with previous releases (#316).

Full Changelog: v0.19.0...v0.19.0.post1

v0.19.0

This release adds sksurv.tree.SurvivalTree.apply() and sksurv.tree.SurvivalTree.decision_path(), and support for sparse matrices to sksurv.tree.SurvivalTree. Moreover, it fixes build issues with scikit-learn 1.1.2 and on macOS with ARM64 CPU.

Bug fixes

• Fix build issue with scikit-learn 1.1.2, which is binary-incompatible with previous releases from the 1.1 series.
• Fix build from source on macOS with ARM64 by specifying numpy 1.21.0 as install requirement for that platform (#313).

Enhancements

• sksurv.tree.SurvivalTree: Add sksurv.tree.SurvivalTree.apply() and sksurv.tree.SurvivalTree.decision_path() (#290).
• sksurv.tree.SurvivalTree: Add support for sparse matrices (#290).

Full Changelog: v0.18.0...v0.19.0

v0.18.0

This release adds support for scikit-learn 1.1, which includes more informative error messages.
Support for Python 3.7 has been dropped, and the minimum supported versions of dependencies are updated to

• numpy 1.17.3
• Pandas 1.0.5
• scikit-learn 1.1.0
• scipy 1.3.2

Enhancements

• Add n_iter_ attribute to all estimators in sksurv.svm (#277).
• Add return_array argument to all models providing
  predict_survival_function and predict_cumulative_hazard_function (#268).

Deprecations

• The loss_ attribute of ComponentwiseGradientBoostingSurvivalAnalysis and GradientBoostingSurvivalAnalysis has been deprecated.
• The default for the max_features argument has been changed from 'auto' to 'sqrt' for RandomSurvivalForest and ExtraSurvivalTrees. 'auto' and 'sqrt' have the same effect.

Full Changelog: v0.17.2...v0.18.0

v0.17.2

This release fixes several issues with packaging scikit-survival.

Bug fixes

• Added backward support for gcc-c++ by @navashiva (#255).
• Do not install C/C++ and Cython source files.
• Add packaging to build requirements in pyproject.toml.
• Exclude generated API docs from source distribution.
• Add Python 3.10 to classifiers.

Documentation

• Use permutation_importance from sklearn instead of eli5.
• Build documentation with Sphinx 4.4.0.
• Fix missing documentation for classes in sksurv.meta.

New Contributors

• @navashiva made their first contribution in #255

Full Changelog: v0.17.1...v0.17.2

v0.17.1

This release adds support for Python 3.10.

Full Changelog: v0.17.0...v0.17.1