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Recent Releases of scikit-survival

scikit-survival - v0.25.0

This release adds support for scikit-learn 1.7 and overhauls the API documentation to improve clarity and consistency.

Enhancements

  • Add support for scikit-learn 1.7 (#532).
  • Move tox configuration to pyproject.toml.
  • Add PEP 735 dependency groups for optional dependencies.
  • Modernize C++ syntax in the coxnet model, improving code clarity and maintainability (#526).
  • Add license-files field to pyproject.toml (PEP 639).
  • Add artifact attestation for sdist and wheel files.
  • Update CI infrastructure to use the latest runners and tools, including check-jsonschema, ruff, and uv.
  • Update CI infrastructure to use miniforge to avoid licensing issues related to Anaconda’s default channels (#542).
  • Add running doctest to CI.
  • Bump versions of dependencies on Binder.

Documentation

  • Overhaul the entire API documentation for improved clarity, consistency, and user experience. This includes updated docstrings for all major modules, including ensemble, linear_model, svm, tree, metrics, and nonparametric (#539).
  • For examples with matplotlib plots, include the plot as a static image in the documentation (#543).
  • Clarify what inputs each metric expects and add a graphical overview to Evaluating Survival Models (#535).
  • Clarify the calculation of the deviance_ratio_ in sksurv.linear_model.CoxnetSurvivalAnalysis with a detailed mathematical definition (#541).
  • Standardize the description of the structured survival array y across the library.
  • Clarify that an exception is raised for out-of-range test times when the censoring distribution cannot be estimated (#524).
  • Explain how the alphas sequence is automatically generated in sksurv.linear_model.CoxnetSurvivalAnalysis.
  • Fix pandas warnings in example code.
  • Update links to external documentation, including scikit-learn and numpy.

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.24.1...v0.25.0

- Python
Published by sebp 10 months ago

scikit-survival - v0.24.1

This release restricts the version of osqp to versions prior to 1.0.0.

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.24.0...v0.24.1

- Python
Published by sebp about 1 year ago

scikit-survival - v0.24.0

This release adds support for scikit-learn 1.6, which includes missing-values support for sksurv.ensemble.ExtraSurvivalTrees. Moreover, the release features sksurv.nonparametric.cumulativeincidencecompeting_risks() which implements a non-parameteric estimator of the cumulative incidence function for competing risks. See the user guide on the analysis of competing risks.

Bug fixes

Enhancements

Documentation

Backwards incompatible changes

  • Support for scikit-learn versions before 1.6.1 has been dropped (#504).
  • Support for Python versions before 3.10 has been dropped.

New Contributors

  • @mvlvrd made their first contribution in https://github.com/sebp/scikit-survival/pull/491

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.23.1...v0.24.0

- Python
Published by sebp over 1 year ago

scikit-survival - v0.23.1

This release adds support for Python 3.13. The minimum required version for pandas has been bumped to pandas 1.4.0.

Bug fixes

Enhancements

Backwards incompatible changes

  • Support for pandas versions before 1.4.0 has been dropped.

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.23.0...v0.23.1

- Python
Published by sebp over 1 year ago

scikit-survival - v0.23.0

This release adds support for scikit-learn 1.4 and 1.5, which includes missing value support for sksurv.ensemble.RandomSurvivalForest.

Moreover, this release fixes critical bugs. When fitting sksurv.tree.SurvivalTree, the sample_weight is now correctly considered when computing the log-rank statistic for each split. This change also affects sksurv.ensemble.RandomSurvivalForest and sksurv.ensemble.ExtraSurvivalTrees which pass sample_weight to the individual trees in the ensemble.

This release fixes a bug in sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis and sksurv.ensemble.GradientBoostingSurvivalAnalysis when dropout is used. Previously, dropout was only applied starting with the third iteration, now dropout is applied in the second iteration too.

Finally, this release adds compatibility with numpy 2.0 and drops support for Python 3.8.

Bug fixes

Enhancements

Documentation

Backwards incompatible changes

  • Drop support for Python 3.8 (#427).

New Contributors

  • @CaderIdris made their first contribution in https://github.com/sebp/scikit-survival/pull/430

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.22.2...v0.23.0

- Python
Published by sebp almost 2 years ago

scikit-survival - 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 iscategoricaldtype() of Pandas API.

Documentation

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.22.1...v0.22.2

- Python
Published by sebp over 2 years ago

scikit-survival - v0.22.1

Bug fixes

- Python
Published by sebp over 2 years ago

scikit-survival - 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 lowmemory 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 `predictcumulativehazardfunctionandpredictsurvivalfunction`.

Bug fixes

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

Enhancements

Documentation

Backwards incompatible changes

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.21.0...v0.22.0

- Python
Published by sebp over 2 years ago

scikit-survival - v0.21.0

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

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

Documentation

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 https://github.com/sebp/scikit-survival/pull/345
  • @cpoerschke made their first contribution in https://github.com/sebp/scikit-survival/pull/358

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.20.0...v0.21.0

- Python
Published by sebp almost 3 years ago

scikit-survival - v0.20.0

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

Enhancements

Documentation

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

Backwards incompatible changes

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.19.0.post1...v0.20.0

- Python
Published by sebp about 3 years ago

scikit-survival - 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: https://github.com/sebp/scikit-survival/compare/v0.19.0...v0.19.0.post1

- Python
Published by sebp over 3 years ago

scikit-survival - 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

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.18.0...v0.19.0

- Python
Published by sebp over 3 years ago

scikit-survival - 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

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.17.2...v0.18.0

- Python
Published by sebp almost 4 years ago

scikit-survival - 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 https://github.com/sebp/scikit-survival/pull/255

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.17.1...v0.17.2

- Python
Published by sebp about 4 years ago

scikit-survival - v0.17.1

This release adds support for Python 3.10.

Full Changelog: https://github.com/sebp/scikit-survival/compare/v0.17.0...v0.17.1

- Python
Published by sebp about 4 years ago

scikit-survival - v0.17.0

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 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()).

- Python
Published by sebp over 4 years ago

scikit-survival - v0.16.0

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.asconcordanceindexipcwscorer, sksurv.metrics.ascumulativedynamicaucscorer, or sksurv.metrics.asintegratedbrierscorescorer to adjust the score method to your needs. A detailed example is available in the User Guide.

Moreover, this release adds sksurv.ensemble.ExtraSurvivalTrees to fit an ensemble of randomized survival trees, and improves the speed of sksurv.compare.compare_survival() significantly. The documentation has been extended by a section on the time-dependent Brier score.

Bug fixes

Enhancements

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 and sksurv.ensemble.RandomSurvivalForest (#217).
  • Add section on time-dependent Brier score (#220).
  • Add section on using alternative metrics for hyper-parameter optimization.

- Python
Published by sebp over 4 years ago

scikit-survival - v0.15.0

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 or sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis are fit with loss='coxph', predict_cumulative_hazard_function and predict_survival_function are now available. sksurv.metrics.cumulativedynamicauc now supports evaluating time-dependent predictions, for instance for a sksurv.ensemble.RandomSurvivalForest as illustrated in the User Guide.

Bug fixes

Enhancements

Backwards incompatible changes

- Python
Published by sebp about 5 years ago

scikit-survival - v0.14.0

This release features a complete overhaul of the documentation. It features a new visual design, and the inclusion of several interactive notebooks in the User Guide.

In addition, it includes important bug fixes. It fixes several bugs in 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 and sksurv.ensemble.GradientBoostingSurvivalAnalysis will now correctly compute the concordance index if loss='ipcwls' or loss='squared'.

Bug fixes

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.
  • Add user guide on gradient boosted models.

- Python
Published by sebp over 5 years ago

scikit-survival - v0.13.1

This release fixes warnings that were introduced with 0.13.0.

Bug fixes

- Python
Published by sebp almost 6 years ago

scikit-survival - v0.13.0

The highlights of this release include the addition of sksurv.metrics.brier_score and sksurv.metrics.integratedbrierscore and compatibility with scikit-learn 0.23.

predict_survival_function and predict_cumulative_hazard_function of sksurv.ensemble.RandomSurvivalForest and sksurv.tree.SurvivalTree can now return an array of sksurv.functions.StepFunction, similar to sksurv.linear_model.CoxPHSurvivalAnalysis by specifying return_array=False. This will be the default behavior starting with 0.14.0.

Note that this release fixes a bug in estimating inverse probability of censoring weights (IPCW), which will affect all estimators relying on IPCW.

Enhancements

Deprecations

Bug fixes

- Python
Published by sebp almost 6 years ago

scikit-survival - v0.12.0

This release adds support for scikit-learn 0.22, thereby dropping support for older versions. Moreover, the regularization strength of the ridge penalty in sksurv.linear_model.CoxPHSurvivalAnalysis can now be set per feature. If you want one or more features to enter the model unpenalized, set the corresponding penalty weights to zero. Finally, sklearn.pipeline.Pipeline will now be automatically patched to add support for predict_cumulative_hazard_function and predict_survival_function if the underlying estimator supports it.

Deprecations

Enhancements

- Python
Published by sebp about 6 years ago

scikit-survival - v0.11

This release adds sksurv.tree.SurvivalTree and sksurv.ensemble.RandomSurvivalForest, which are based on the log-rank split criterion. It also adds the OSQP solver as option to sksurv.svm.MinlipSurvivalAnalysis and sksurv.svm.HingeLossSurvivalSVM, which will replace the now deprecated cvxpy and cvxopt options in a future release.

This release removes support for sklearn 0.20 and requires sklearn 0.21.

Deprecations

Enhancements

Bug fixes

  • Exclude Cython-generated files from source distribution because they are not forward compatible.

- Python
Published by sebp over 6 years ago

scikit-survival - v0.10

This release adds the ties argument to sksurv.linear_model.CoxPHSurvivalAnalysis to choose between Breslow’s and Efron’s likelihood in the presence of tied event times. Moreover, sksurv.compare.compare_survival() has been added, which implements the log-rank hypothesis test for comparing the survival function of 2 or more groups.

Enhancements

  • Update API doc of predict function of boosting estimators (#75).
  • Clarify documentation for GradientBoostingSurvivalAnalysis (#78).
  • Implement Efron’s likelihood for handling tied event times.
  • Implement log-rank test for comparing survival curves.
  • Add support for scipy 1.3.1 (#66).

Bug fixes

- Python
Published by sebp over 6 years ago

scikit-survival - v0.9

This release adds support for sklearn 0.21 and pandas 0.24.

Enhancements

  • Add reference to IPCRidge (#65).
  • Use scipy.special.comb instead of deprecated scipy.misc.comb.
  • Add support for pandas 0.24 and drop support for 0.20.
  • Add support for scikit-learn 0.21 and drop support for 0.20 (#71).
  • Explain use of intercept in ComponentwiseGradientBoostingSurvivalAnalysis (#68)
  • Bump Eigen to 3.3.7.

Bug fixes

  • Disallow scipy 1.3.0 due to scipy regression (#66).

- Python
Published by sebp almost 7 years ago

scikit-survival - v0.8

Enhancements

  • Add sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function and sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function (#46).
  • Add sksurv.nonparametric.SurvivalFunctionEstimator and sksurv.nonparametric.CensoringDistributionEstimator that wrap sksurv.nonparametric.kaplan_meier_estimator and provide a predict_proba method for evaluating the estimated function on test data.
  • Implement censoring-adjusted C-statistic proposed by Uno et al. (2011) in sksurv.metrics.concordance_index_ipcw.
  • Add estimator of cumulative/dynamic AUC of Uno et al. (2007) in sksurv.metrics.cumulative_dynamic_auc.
  • Add flchain dataset (see sksurv.datasets.load_flchain).

Bug fixes

  • The tied_time return value of sksurv.metrics.concordance_index_censored now correctly reflects the number of comparable pairs that share the same time and that are used in computing the concordance index.
  • Fix a bug in sksurv.metrics.concordance_index_censored where a pair with risk estimates within tolerance was counted both as concordant and tied.

- Python
Published by sebp about 7 years ago

scikit-survival - v0.7

This release adds support for Python 3.7 and sklearn 0.20.

Changes: * Add support for sklearn 0.20 (#48). * Migrate to py.test (#50). * Explicitly request ECOS solver for sksurv.svm.MinlipSurvivalAnalysis and sksurv.svm.HingeLossSurvivalSVM. * Add support for Python 3.7 (#49). * Add support for cvxpy >=1.0. * Add support for numpy 1.15.

- Python
Published by sebp over 7 years ago

scikit-survival - v0.6.0

This release adds support for numpy 1.14 and pandas up to 0.23. In addition, the new class sksurv.util.Surv makes it easier to construct a structured array from numpy arrays, lists, or a pandas data frame.

Changes:

  • Support numpy 1.14 and pandas 0.22, 0.23 (#36).
  • Enable support for cvxopt with Python 3.5+ on Windows (requires cvxopt >=1.1.9).
  • Add max_iter parameter to sksurv.svm.MinlipSurvivalAnalysis and sksurv.svm.HingeLossSurvivalSVM.
  • Fix score function of sksurv.svm.NaiveSurvivalSVM to use concordance index.
  • sksurv.linear_model.CoxnetSurvivalAnalysis now throws an exception if coefficients get too large (#47).
  • Add sksurv.util.Surv class to ease constructing a structured array (#26).

- Python
Published by sebp over 7 years ago

scikit-survival - v0.5

This release adds support for scikit-learn 0.19 and pandas 0.21. In turn, support for older versions is dropped, namely Python 3.4, scikit-learn 0.18, and pandas 0.18.

- Python
Published by sebp over 8 years ago

scikit-survival - v0.4

This release adds sksurv.linear_model.CoxnetSurvivalAnalysis which implements an efficient algorithm to fit Cox's proportional hazards model with LASSO, ridge, and elastic net penalty. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.

- Python
Published by sebp over 8 years ago

scikit-survival - v0.3

This release adds predict_survival_function and predict_cumulative_hazard_function to sksurv.linear_model.CoxPHSurvivalAnalysis, which return the survival function and cumulative hazard function using Breslow's estimator.

Moreover, it fixes a build error on Windows (#3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.

- Python
Published by sebp almost 9 years ago

scikit-survival -

This release adds support for Python 3.6, and pandas 0.19 and 0.20.

- Python
Published by sebp about 9 years ago