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Interpretable Machine Learning for Survival Analysis Sophie Hanna Langbein, Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek, Marvin N. Wright Author of the code: Sophie Hanna Langbein (langbein@leibniz-bips.de, sophie_langbein@gmx.de) The R code can be found in utils.R, plotting_functions.R, simulation_example_ice_pdp.R and simulation_example_ale.R, simulation_example_fi.R, real_data_example.R The code files need to be run in the following order: simulation_example_ice_pdp.R -> simulation_example_ale.R -> simulation_example_fi.R -> real_data_example.R Files and Folder structure: Folders: data: folder containing the data to be analyzed figures_iml: folder in which all figures generated in the code and contained in the paper are saved files: utils.R: code file containing helper functions plotting_functions.R: code file containing plotting functions simulation_example_ice_pdp.R: code file containing the simulation example for permutation feature importance, individual conditional expectation plots and partial dependence plots in the methods section of the paper simulation_example_ale.R: code file containing the simulation example for accumulated local effects plots in the methods section of the paper simulation_example_fi.R: code file containing the simulation example for the feature interaction H-statistics plots in the methods section of the paper real_data_example.R: code file containing the example of an IML analysis on real data (GBSG2 dataset) The code was produced with the following versions of R and packages: R version 4.4.0 (2024-04-24) Platform: aarch64-apple-darwin20 Running under: macOS 15.0.1 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0 locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 time zone: Europe/Berlin tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] ranger_0.16.0 simsurv_1.0.0 data.table_1.15.4 [4] dplyr_1.1.4 ggbeeswarm_0.7.2 ggnewscale_0.5.0 [7] survminer_0.4.9 ggpubr_0.6.0 survAUC_1.3-0 [10] ggplot2_3.5.1 survex_1.2.0 randomForestSRC_3.3.1 [13] pec_2023.04.12 prodlim_2023.08.28 survival_3.5-8 loaded via a namespace (and not attached): [1] gridExtra_2.3 sandwich_3.1-0 rlang_1.1.4 magrittr_2.0.3 [5] multcomp_1.4-25 polspline_1.1.25 compiler_4.4.0 vctrs_0.6.5 [9] quantreg_5.98 stringr_1.5.1 pkgconfig_2.0.3 fastmap_1.2.0 [13] backports_1.5.0 KMsurv_0.1-5 utf8_1.2.4 rmarkdown_2.27 [17] MatrixModels_0.5-3 purrr_1.0.2 xfun_0.44 jsonlite_1.8.8 [21] timereg_2.0.5 broom_1.0.6 parallel_4.4.0 data.tree_1.1.0 [25] cluster_2.1.6 R6_2.5.1 stringi_1.8.4 RColorBrewer_1.1-3 [29] parallelly_1.37.1 car_3.1-2 rpart_4.1.23 numDeriv_2016.8-1.1 [33] Rcpp_1.0.12 iterators_1.0.14 knitr_1.47 future.apply_1.11.2 [37] zoo_1.8-12 base64enc_0.1-3 Matrix_1.7-0 splines_4.4.0 [41] nnet_7.3-19 tidyselect_1.2.1 rstudioapi_0.16.0 abind_1.4-5 [45] codetools_0.2-20 listenv_0.9.1 lattice_0.22-6 tibble_3.2.1 [49] withr_3.0.0 evaluate_0.24.0 foreign_0.8-86 future_1.33.2 [53] survMisc_0.5.6 pillar_1.9.0 carData_3.0-5 DiagrammeR_1.0.11 [57] checkmate_2.3.1 foreach_1.5.2 generics_0.1.3 munsell_0.5.1 [61] scales_1.3.0 globals_0.16.3 xtable_1.8-4 glue_1.7.0 [65] rms_6.8-1 Hmisc_5.1-3 tools_4.4.0 SparseM_1.83 [69] ggsignif_0.6.4 visNetwork_2.1.2 mvtnorm_1.2-5 grid_4.4.0 [73] DALEX_2.4.3 tidyr_1.3.1 colorspace_2.1-0 nlme_3.1-164 [77] patchwork_1.2.0 beeswarm_0.4.0 htmlTable_2.4.2 vipor_0.4.7 [81] Formula_1.2-5 cli_3.6.2 km.ci_0.5-6 fansi_1.0.6 [85] lava_1.8.0 gtable_0.3.5 rstatix_0.7.2 digest_0.6.35 [89] TH.data_1.1-2 htmlwidgets_1.6.4 htmltools_0.5.8.1 lifecycle_1.0.4 [93] MASS_7.3-60.2
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This repository contains code accompanying the paper "Interpretable Machine Learning for Survival Analysis"
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