Scikit-survival

This release adds [sksurv.tree.SurvivalTree.apply()](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree.apply) and [sksurv.tree.SurvivalTree.decision_path()](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree.decision_path), and support for sparse matrices to [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#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](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree): Add [sksurv.tree.SurvivalTree.apply()](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree.apply) and [sksurv.tree.SurvivalTree.decision_path()](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree.decision_path) (290).
- [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/v0.19.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree): Add support for sparse matrices (290).

**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
- 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](https://scikit-survival.readthedocs.io/en/v0.18.0/api/svm.html#mod-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](https://scikit-survival.readthedocs.io/en/latest/api/generated/sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis.html#sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis) and [GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.18.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis) has been deprecated.
- The default for the ``max_features`` argument has been changed from ``'auto'`` to ``'sqrt'`` for [RandomSurvivalForest](https://scikit-survival.readthedocs.io/en/v0.18.0/api/generated/sksurv.ensemble.RandomSurvivalForest.html#sksurv.ensemble.RandomSurvivalForest) and [ExtraSurvivalTrees](https://scikit-survival.readthedocs.io/en/v0.18.0/api/generated/sksurv.ensemble.ExtraSurvivalTrees.html#sksurv.ensemble.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.

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](https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html#sklearn.inspection.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 https://github.com/sebp/scikit-survival/pull/255

**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

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](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#feature-names-support) 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 bundeled 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 sciki-learn pipeline: `make_pipeline(StandardScaler(with_mean=False), IPCRidge())`.

This release adds support for changing the evaluation metric that is used in estimators’ `score` method. This is particular useful for hyper-parameter optimization using scikit-learn’s `GridSearchCV`. You can now use [sksurv.metrics.as_concordance_index_ipcw_scorer](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.metrics.as_concordance_index_ipcw_scorer.html#sksurv.metrics.as_concordance_index_ipcw_scorer "sksurv.metrics.as_concordance_index_ipcw_scorer"), [sksurv.metrics.as_cumulative_dynamic_auc_scorer](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.metrics.as_cumulative_dynamic_auc_scorer.html#sksurv.metrics.as_cumulative_dynamic_auc_scorer "sksurv.metrics.as_cumulative_dynamic_auc_scorer"), or [sksurv.metrics.as_integrated_brier_score_scorer](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.metrics.as_integrated_brier_score_scorer.html#sksurv.metrics.as_integrated_brier_score_scorer "sksurv.metrics.as_integrated_brier_score_scorer") to adjust the `score` method to your needs. A detailed example is available in the [User Guide](https://scikit-survival.readthedocs.io/en/v0.16.0/user_guide/evaluating-survival-models.html#Using-Metrics-in-Hyper-parameter-Search).

Moreover, this release adds [sksurv.ensemble.ExtraSurvivalTrees](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.ensemble.ExtraSurvivalTrees.html#sksurv.ensemble.ExtraSurvivalTrees "sksurv.ensemble.ExtraSurvivalTrees") to fit an ensemble of randomized survival trees, and improves the speed of [sksurv.compare.compare_survival()](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.compare.compare_survival.html#sksurv.compare.compare_survival "sksurv.compare.compare_survival") significantly. The documentation has been extended by a section on the [time-dependent Brier score](https://scikit-survival.readthedocs.io/en/v0.16.0/user_guide/evaluating-survival-models.html#Time-dependent-Brier-Score).

Bug fixes

- Columns are dropped in [sksurv.column.encode_categorical()](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.column.encode_categorical.html#sksurv.column.encode_categorical "sksurv.column.encode_categorical") despite `allow_drop=False` (199).
- Ensure [sksurv.column.categorical_to_numeric()](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.column.categorical_to_numeric.html#sksurv.column.categorical_to_numeric "sksurv.column.categorical_to_numeric") always returns series with int64 dtype.


Enhancements

- Add [sksurv.ensemble.ExtraSurvivalTrees](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.ensemble.ExtraSurvivalTrees.html#sksurv.ensemble.ExtraSurvivalTrees "sksurv.ensemble.ExtraSurvivalTrees") ensemble (195).
- Faster speed for [sksurv.compare.compare_survival()](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.compare.compare_survival.html#sksurv.compare.compare_survival "sksurv.compare.compare_survival") (215).
- Add wrapper classes [sksurv.metrics.as_concordance_index_ipcw_scorer](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.metrics.as_concordance_index_ipcw_scorer.html#sksurv.metrics.as_concordance_index_ipcw_scorer "sksurv.metrics.as_concordance_index_ipcw_scorer"), [sksurv.metrics.as_cumulative_dynamic_auc_scorer](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.metrics.as_cumulative_dynamic_auc_scorer.html#sksurv.metrics.as_cumulative_dynamic_auc_scorer "sksurv.metrics.as_cumulative_dynamic_auc_scorer"), and [sksurv.metrics.as_integrated_brier_score_scorer](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.metrics.as_integrated_brier_score_scorer.html#sksurv.metrics.as_integrated_brier_score_scorer "sksurv.metrics.as_integrated_brier_score_scorer") to override the default `score` method of estimators (192).
- Remove use of deprecated numpy dtypes.
- Remove use of `inplace` in pandas’ `set_categories`.


Documentation

- Remove comments and code suggesting log-transforming times prior to training Survival SVM (203).
- Add documentation for `max_samples` parameter to [sksurv.ensemble.ExtraSurvivalTrees](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.ensemble.ExtraSurvivalTrees.html#sksurv.ensemble.ExtraSurvivalTrees "sksurv.ensemble.ExtraSurvivalTrees") and [sksurv.ensemble.RandomSurvivalForest](https://scikit-survival.readthedocs.io/en/v0.16.0/api/generated/sksurv.ensemble.RandomSurvivalForest.html#sksurv.ensemble.RandomSurvivalForest "sksurv.ensemble.RandomSurvivalForest") (217).
- Add section on time-dependent Brier score (220).
- Add section on using alternative metrics for hyper-parameter optimization.