GADGET: Generalized Additive Decomposition of Global EffecTs

This repository gives access to an implementation of the methods presented in the paper “Decomposing Global Feature Effects Based on Feature Interactions”, as well as all code that was used for the experiments and the real-world examples.

This repository is structured as follows:

    ├── R/                       # All implemented methods and general helper functions  
    |   ├── analysis/            # Scripts used to create figures and tables in the paper for simulation examples (Sec. 4 - 7 and Appendix)
    |   ├── applications/        # Scripts used for modelling and to create figures for real-world examples in Section 7
    |   ├── simulations/         # helper functions for simulations
    |   |   ├── batchtools/      # Scripts used to create data for simulation experiments (Sec. 6.1-6.3, Sec. 7 and Appendix E)
    ├── data/                    # Location where all generated data are stored
    │   ├── application/         # Location where datasets for real-world examples are stored
    │   ├── batchtools/          # Location where data generated by simulation experiments are stored
    |   figures/                 # Location where all figures are stored
    ├── LICENSE
    └── README.md               

Reproduce Experiments

Steps to reproduce the experiments of Sections 6.1 to 6.3, 7 and respective Appendices.

1. Install all required packages.

# from CRAN
install.packages(c("ranger", "dplyr", "batchtools", "mlr", "ggplot2", "tidyr", "reshape2",
"ggpubr", "BBmisc", "data.table", "stringi", "stringr", "checkmate", "kernlab", "xtable", "devtools",
"tidyverse", "Rmalschains", "iml", "dtw","egg","rlist","mgcv","mvtnorm", "vip", "data.table",
"e1071", "RColorBrewer", "R6", "mlr3", "mlr3learners", "xgboost", "fastshap", "patchwork", "nnet"))

2. Create an experimental registry, add experiments and problem and run simulations via script R/simulations/generate_data_*.R where * stands for cor (Sec. 6.1), higher_order (Sec. 6.2), pint (Sec. 6.3) or pint_eval (Sec. 7 and App. E). Data produced by the scripts is stored in the subfolder data/batchtools/ as a separate registry.

3. Prepare data for analysis by running the script R/simulations/reduce_experiments_*.R where * stands for cor (Sec. 6.1), higher_order (Sec. 6.2), pint (Sec. 6.3) or pint_eval (Sec. 7 and App. E).

4. To reproduce figures and tables of Section 6 and respective Appendices, run the scripts in folder R/analysis/. Figures produced within the script are stored in figures.

To reproduce the experiments of Section 4 and Appendix B, run the scripts R/analysis/create_plots_sec_3_and_4.R. To reproduce the results of the real-world examples, run the scripts in R/applications/