/survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Primary LanguageJupyter NotebookMIT LicenseMIT

SurvSHAP(t)

This repository contains data and code for the article:

M. Krzyziński, M. Spytek, H. Baniecki, P. Biecek. SurvSHAP(t): Time-dependent explanations of machine learning survival models. Knowledge-Based Systems, 262:110234, 2023. https://doi.org/10.1016/j.knosys.2022.110234

@article{,
    title = {SurvSHAP(t): Time-dependent explanations of machine learning survival models},
    author = {Mateusz Krzyziński and Mikołaj Spytek and Hubert Baniecki and Przemysław Biecek},
    journal = {Knowledge-Based Systems},
    volume = {262},
    pages = {110234},
    year = {2023}
}

NOTE: SurvSHAP(t) and SurvLIME are also implemented in the survex R package

Python version: 3.10.5

Methods

  • survshap directory contains the SurvSHAP(t) method implementation (NOTE: it can be installed as package - setup.py)
  • survlime.py is the SurvLIME method implementation
  • survnam directory contains the SurvNAM method implementation (based on Jia-Xiang Chengh implementation)

Data

  • data_generation.R is the code for synthetic censored data generation (for Experiments 1 and 2)
  • data directory contains the datasets used in experiments

Experiments

  • experiments directory contains Jupyter Notebooks (*.ipynb files) with code of the conducted experiments
  • results directory contains results of the conducted experiments

Plots

  • plots.R is the code for creating Figures from the article
  • plots directory contains Figures in .pdf format