Tsuyoshi (Ide-san) Ide
IBM Thomas J. Research Center, tide@us.ibm.com / ide@ide-research.net
This repository provides an end-to-end demo of a Granger-causal analysis approach proposed in
Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe, "Cardinality-Regularized Hawkes-Granger Model," Advances in Neural Information Processing Systems 34 (NeurIPS 2021). (slides).
This Jupyter notebook provides full details (tested on 06/10/2024 on a clean pip environment with libraries described in requirements.txt). Enjoy!
The core module L0HawkesLib.py depends only on numpy. This is the only critical dependency of this Python implementation of the L0Hawkes algorithm.
For visualization purposes, a utility library called L0HawkesVisualize.py has been included, which depends on:
pandasmatplotlibseaborn
If you want to reproduce the notebook as it is, I suggest running
pip install numpy pandas matplotlib seabornon a clean pip environment so all the sub-libraries will be automatically installed.
If you are not comfortable with installing these, just download only L0HawkesLib.py and use it in your environment.