Source code for Transformer Hawkes Process (ICML 2020).
- Put the data folder inside the root folder, modify the data entry in run.sh accordingly. The datasets are available here.
- bash run.sh to run the code.
- Right now the code only supports single GPU training, but an extension to support multiple GPUs should be easy.
- The reported event time prediction RMSE and the time stamps provided in the datasets are not of the same unit, i.e., the provided time stamps can be in minutes, but the reported results are in hours.
- There are several factors that can be changed, beside the ones in run.sh:
- In Main.py, function train_epoch, the event time prediction squared error needs to be properly scaled to stabilize training.
- In Utils.py, function log_likelihood, users can select whether to use numerical integration or Monte Carlo integration.
- In transformer/Models.py, class Transformer, parameter alpha controls the weight of the time difference factor. This parameter can be added into the computation graph, i.e., changeable during training, but the gain is marginal.
- In transformer/Models.py, class Transformer, there is an optional recurrent layer. This is inspired by the fact that additional recurrent layers can better capture the sequential context, as suggested in this paper. In reality, this may or may not help, depending on the dataset.
Please cite the following paper if you use this code.
@article{zuo2020transformer,
title={Transformer Hawkes Process},
author={Zuo, Simiao and Jiang, Haoming and Li, Zichong and Zhao, Tuo and Zha, Hongyuan},
journal={arXiv preprint arXiv:2002.09291},
year={2020}
}