Reproducibility package for the paper:
Lucas Maystre, Daniel Russo. Temporally-Consistent Survival Analysis. Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
This repository contains
- a reference implementation of the algorithms presented in the paper, and
- Jupyter notebooks enabling the reproduction of some of the experiments.
The paper and the libary address the problem of learning survival models from sequential observations (also known as the dynamic setting). For an accessible overview of the main idea, you can read our blog post.
To get started, follow these steps:
- Clone the repo locally with:
git clone https://github.com/spotify-research/tdsurv.git - Move to the repository:
cd tdsurv - Install the dependencies:
pip install -r requirements.txt - Install the package:
pip install -e lib/ - Move to the notebook folder:
cd notebooks - Start a notebook server:
jupyter notebok
To reproduce some of the experimental results, you will need to download the
relevant datasets. You can find further instructions under data/README.md.
Our codebase was tested with Python 3.9.7. The following libraries are required
(and installed automatically via the first pip command above):
- jax (tested with version 0.3.4)
- jaxlib (tested with version 0.3.2)
- jupyter (tested with version 6.4)
- lifelines (tested with version 0.27.0)
- matplotlib (tested with version 3.5.1)
- numpy (tested with version 1.21.2)
- pandas (tested with version 1.4.1)
- scipy (tested with version 1.73)
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