Scikit-survival

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.

This release adds support for scikit-learn 0.24 and Python 3.9. scikit-survival now requires at least pandas 0.25 and scikit-learn 0.24. Moreover, if [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis) or [sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis.html#sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis) are fit with `loss='coxph'`, <span class="title-ref">predict\_cumulative\_hazard\_function</span> and <span class="title-ref">predict\_survival\_function</span> are now available. [sksurv.metrics.cumulative_dynamic_auc](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.metrics.cumulative_dynamic_auc.html) now supports evaluating time-dependent predictions, for instance for a [sksurv.ensemble.RandomSurvivalForest](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.RandomSurvivalForest.html) as illustrated in the [User Guide](https://scikit-survival.readthedocs.io/en/v0.15.0/user_guide/evaluating-survival-models.html#Using-Time-dependent-Risk-Scores).

Bug fixes

- Allow passing pandas data frames to all `fit` and `predict` methods (\148).
- Allow sparse matrices to be passed to [sksurv.ensemble.GradientBoostingSurvivalAnalysis.predict](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis.predict).
- Fix example in user guide using GridSearchCV to determine alphas for CoxnetSurvivalAnalysis (\186).

Enhancements

- Add score method to [sksurv.meta.Stacking](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.meta.Stacking.html), [sksurv.meta.EnsembleSelection](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.meta.EnsembleSelection.html), and [sksurv.meta.EnsembleSelectionRegressor](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.meta.EnsembleSelectionRegressor.html) (\#151).
- Add support for <span class="title-ref">predict\_cumulative\_hazard\_function</span> and <span class="title-ref">predict\_survival\_function</span> to [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis). and [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis.html#sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis) if model was fit with `loss='coxph'`.
- Add support for time-dependent predictions to [sksurv.metrics.cumulative_dynamic_auc](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.metrics.cumulative_dynamic_auc.html) See the [User Guide](https://scikit-survival.readthedocs.io/en/v0.15.0/user_guide/evaluating-survival-models.html#Using-Time-dependent-Risk-Scores) for an example (\134).

Backwards incompatible changes

- The score method of [sksurv.linear_model.IPCRidge](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.linear_model.IPCRidge.html), [sksurv.svm.FastSurvivalSVM](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.svm.FastSurvivalSVM.html), and [sksurv.svm.FastKernelSurvivalSVM](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.svm.FastKernelSurvivalSVM.html) (if `rank_ratio` is smaller than 1) now converts predictions on log(time) scale to risk scores prior to computing the concordance index.
- Support for cvxpy and cvxopt solver in [sksurv.svm.MinlipSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.svm.MinlipSurvivalAnalysis.html) and [sksurv.svm.HingeLossSurvivalSVM](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.svm.HingeLossSurvivalSVM.html) has been dropped. The default solver is now ECOS, which was used by cvxpy (the previous default) internally. Therefore, results should be identical.
- Dropped the `presort` argument from [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.tree.SurvivalTree.html) and [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis).
- The `X_idx_sorted` argument in [sksurv.tree.SurvivalTree.fit](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.tree.SurvivalTree.html#sksurv.tree.SurvivalTree.fit) has been deprecated in scikit-learn 0.24 and has no effect now.
- <span class="title-ref">predict\_cumulative\_hazard\_function</span> and <span class="title-ref">predict\_survival\_function</span> of [sksurv.ensemble.RandomSurvivalForest](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.ensemble.RandomSurvivalForest.html) and [sksurv.tree.SurvivalTree](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.tree.SurvivalTree.html) now return an array of [sksurv.functions.StepFunction](https://scikit-survival.readthedocs.io/en/v0.15.0/api/generated/sksurv.functions.StepFunction.html) objects by default. Use `return_array=True` to get the old behavior.
- Support for Python 3.6 has been dropped.
- Increase minimum supported versions of dependencies. We now require:

> | Package | Minimum Version |
> |--------------|-----------------|
> | Pandas | 0.25.0 |
> | scikit-learn | 0.24.0 |

This release features a complete overhaul of the [documentation](https://scikit-survival.readthedocs.io/en/v0.14.0/index.html). It features a new visual design, and the inclusion of several interactive notebooks in the [User Guide](https://scikit-survival.readthedocs.io/en/v0.14.0/user_guide/index.html).

In addition, it includes important bug fixes. It fixes several bugs in [sksurv.linear_model.CoxnetSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.linear_model.CoxnetSurvivalAnalysis.html#sksurv.linear_model.CoxnetSurvivalAnalysis) where `predict`, `predict_survival_function`, and `predict_cumulative_hazard_function` returned wrong values if features of the training data were not centered. Moreover, the score function of [sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis.html#sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis) and [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis) will now correctly compute the concordance index if `loss='ipcwls'` or `loss='squared'`.


Bug fixes

- [sksurv.column.standardize()](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.column.standardize.html#sksurv.column.standardize) modified data in-place. Data is now always copied.
- [sksurv.column.standardize()](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.column.standardize.html#sksurv.column.standardize) works with integer numpy arrays now.
- [sksurv.column.standardize()](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.column.standardize.html#sksurv.column.standardize) used biased standard deviation for numpy arrays (`ddof=0`), but unbiased standard deviation for pandas objects (`ddof=1`). It always uses `ddof=1` now. Therefore, the output, if the input is a numpy array, will differ from that of previous versions.
- Fixed [sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function()](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.linear_model.CoxnetSurvivalAnalysis.html#sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function) and [sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function()](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.linear_model.CoxnetSurvivalAnalysis.html#sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function), which returned wrong values if features of training data were not already centered. This adds an offset_ attribute that accounts for non-centered data and is added to the predicted risk score. Therefore, the outputs of `predict`, `predict_survival_function`, and `predict_cumulative_hazard_function` will be different to previous versions for non-centered data (139).
- Rescale coefficients of [sksurv.linear_model.CoxnetSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.linear_model.CoxnetSurvivalAnalysis.html#sksurv.linear_model.CoxnetSurvivalAnalysis) if `normalize=True`.
- Fix score function of [sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis.html#sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis) and [sksurv.ensemble.GradientBoostingSurvivalAnalysis](https://scikit-survival.readthedocs.io/en/v0.14.0/api/generated/sksurv.ensemble.GradientBoostingSurvivalAnalysis.html#sksurv.ensemble.GradientBoostingSurvivalAnalysis) if `loss='ipcwls'` or `loss='squared'` is used. Previously, it returned `1.0 - true_cindex`.

Enhancements

- Add `sksurv.show_versions()` that prints the version of all dependencies.
- Add support for pandas 1.1
- Include interactive notebooks in documentation on readthedocs.
- Add user guide on [penalized Cox models](https://scikit-survival.readthedocs.io/en/v0.14.0/user_guide/coxnet.html).
- Add user guide on [gradient boosted models](https://scikit-survival.readthedocs.io/en/v0.14.0/user_guide/boosting.html).