Welcome to the repository for "Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis", to appear at AISTATS 2022.
Create a virtual environment and activate it:
python -m venv desurv_env
source desurv_env/bin/activate
Install dependencies:
python -m pip install -r requirements.txt
Access the source code:
cd src
Use DeCDF for CDF/density estimation:
python -m jupyter notebook fig2.ipynb
Train a DeSurv model:
python desurv.py
The core code components of DeCDF/DeSurv can be found in classes.py
.
The ODESurvSingle
class implements a DeSurv model for the single-risk setting.
The ODESurvMultiple
class implements a DeSurv model for the competing-risk setting.
desurv.py
illustrates how a DeSurv model can be trained and is designed to be easily
adaptable to a typical user's problem setting. fig2.ipynb
illustrates the basic concept
behind DeCDF and hence DeSurv in an interactive setting.
We would like to acknowledge the pycox library of Kvamme et al. for its excellent dataset, model and evaluation provisions which proved very useful when undertaking this project.