Survival analysis built on top of scikit-learn
This release adds support for Python 3.12.
Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.22.1...v0.22.2
X
has missing values and dtype other than float32 (#412).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
.
predict_log_proba
(#380).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).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: https://github.com/sebp/scikit-survival/compare/v0.21.0...v0.22.0
This is a major release bringing new features and performance improvements.
conf_type
parameter.predict_cumulative_hazard_function
or predict_survival_function
(#375).conf_type
parameter (#348)._predict_risk_score
attribute in sklearn.pipeline.Pipeline if the final estimator of the pipeline has such property (#374).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of estimators has been replaced by unique_times_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).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).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).Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.20.0...v0.21.0
This release adds support for scikit-learn 1.2 and drops support for previous versions.
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: https://github.com/sebp/scikit-survival/compare/v0.19.0.post1...v0.20.0
This release raises the install requirement of scikit-learn to 1.1.2 to avoid binary incompatibility with previous releases (#316).
Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.19.0...v0.19.0.post1
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.
Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.18.0...v0.19.0
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
n_iter_
attribute to all estimators in sksurv.svm (#277).return_array
argument to all models providing
predict_survival_function
and predict_cumulative_hazard_function
(#268).loss_
attribute of ComponentwiseGradientBoostingSurvivalAnalysis and GradientBoostingSurvivalAnalysis has been deprecated.max_features
argument has been changed from 'auto'
to 'sqrt'
for RandomSurvivalForest and ExtraSurvivalTrees. 'auto'
and 'sqrt'
have the same effect.Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.17.2...v0.18.0
This release fixes several issues with packaging scikit-survival.
packaging
to build requirements in pyproject.toml
.sksurv.meta
.Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.17.1...v0.17.2
This release adds support for Python 3.10.
Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.17.0...v0.17.1