SMURF-THP

Code for SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process, ICML 2023.

Dependencies

• Python 3.7.
• Anaconda contains all the required packages.
• PyTorch version 1.8.0.

Instructions

• Put the data folder inside the root folder, modify the data entry in .sh files accordingly. The datasets are available here.

cd scripts/so
# train and evaluate the model
bash smurf_so.sh

Metric: Calibration Score

• Obtain samples from langevin dynamics
• Get a confidence interval given a quantile(e.g 0.8)
• Calculate the ratio of true time falling in the interval(e.g 0.78)
• Calculate RMSE between the ratio and the quantile(e.g quantile: 0.1,0.2,...,0.9; ratio: 0.05,0.18,...,0.91)

Reference

Please cite the following paper if you use this code.

@inproceedings{li2023smurf,
  title={SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process},
  author={Li, Zichong and Xu, Yanbo and Zuo, Simiao and Jiang, Haoming and Zhang, Chao and Zhao, Tuo and Zha, Hongyuan},
  booktitle = {{ICML}},
  year={2023}
}