What’s new in 0.17#

scikit-survival 0.17.2 (2022-04-24)#

This release fixes several issues with packaging scikit-survival.

Bug fixes#

Added backward support for gcc-c++ (#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.

scikit-survival 0.17.1 (2022-03-05)#

This release adds support for Python 3.10.

scikit-survival 0.17.0 (2022-01-09)#

This release adds support for scikit-learn 1.0, which includes support for feature names. If you pass a pandas dataframe to fit, the estimator will set a feature_names_in_ attribute containing the feature names. When a dataframe is passed to predict, it is checked that the column names are consistent with those passed to fit. See the scikit-learn release highlights for details.

Bug fixes#

Fix a variety of build problems with LLVM (#243).

Enhancements#

Add support for feature_names_in_ and n_features_in_ to all estimators and transforms.
Add sksurv.preprocessing.OneHotEncoder.get_feature_names_out().
Update bundled version of Eigen to 3.3.9.

Backwards incompatible changes#

Drop min_impurity_split parameter from sksurv.ensemble.GradientBoostingSurvivalAnalysis.
base_estimators and meta_estimator attributes of sksurv.meta.Stacking do not contain fitted models anymore, use estimators_ and final_estimator_, respectively.

Deprecations#

The normalize parameter of sksurv.linear_model.IPCRidge is deprecated and will be removed in a future version. Instead, use a scikit-learn pipeline: make_pipeline(StandardScaler(with_mean=False), IPCRidge()).