ray-yang521/neurawkes

Source code of The Neural Hawkes Process (NIPS 2017)

The Neural Hawkes Process

Source code for The Neural Hawkes Process (NeurIPS 2017) runnable on GPU and CPU.

Please check out The Neural Hawkes Particle Smoothing for PyTorch Implementation (and more).

Reference

If you use this code as part of any published research, please acknowledge the following paper (it encourages researchers who publish their code!):

The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Hongyuan Mei and Jason Eisner

@inproceedings{mei2017neuralhawkes,
  author =      {Hongyuan Mei and Jason Eisner},
  title =       {The Neural {H}awkes Process: {A} Neurally Self-Modulating Multivariate Point Process},
  booktitle =   {Advances in Neural Information Processing Systems},
  year =        {2017},
  month =       dec,
  address =     {Long Beach},
  url =         {https://arxiv.org/abs/1612.09328}
}

Instructions

Here are the instructions to use the code base

Dependencies

This code is written in python. To use it you will need:

• Anaconda - Anaconda includes all the Python-related dependencies
• Theano - Computational graphs are built on Theano

Prepare Data

Download datasets to the 'data' folder

Train Models

To train the model, try the command line below for detailed guide:

python train_models.py --help

Test Models

To evaluate (dev or test) and save results, use the command line below for detailed guide:

python test_models_and_save.py --help

Generate Sequences

To generate sequences (with trained or randomly initialized models), try the command line:

python generate_sequences.py --help

Significant Tests

To test statistical significance by boostrapping over dev/test set, try the command line:

python generate_sequences.py --help

License

This project is licensed under the MIT License - see the LICENSE file for details