https://github.com/barahona-research-group/streitberg-information
Code for "Information-Theoretic Measures on Lattices for High-Order Interactions"
https://github.com/barahona-research-group/streitberg-information
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Repository
Code for "Information-Theoretic Measures on Lattices for High-Order Interactions"
Basic Info
- Host: GitHub
- Owner: barahona-research-group
- Language: Python
- Default Branch: main
- Size: 136 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Streitberg Information: Higher-Order Interaction Measures
This repository provides an implementation of Streitberg information, an information-theoretic measure for quantifying higher-order interactions from observational data. The codebase also supports related measures such as Lancaster information and total correlation for interactions of order 2, 3, 4, and 5.
Usage
Jupyter Tutorial:
Seetutorial.ipynbfor a step-by-step guide on using Streitberg information measures with a toy example.Core Implementation:
hoi_info.py: Main implementation of Streitberg, Lancaster, and total correlation measures.synthetic_data.py: Functions to generate synthetic higher-order datasets (e.g., XOR gate, COPY gate, Multivariate Gaussian).
Example:
python from hoi_info import Streitberg_4 from ite.cost.x_factory import co_factory co = co_factory(cost_name='BDTsallis_KnnK', mult=True, alpha=0.5, k=30) data = np.random.randn(100, 4) si = Streitberg_4(data, co.estimation)
Repository Structure
ite/: Contains code from the ITE Python package (ITE on Bitbucket), including functions for estimating Tsallis-alpha divergence.hoi_info.py: Core implementation of information measures.synthetic_data.py: Synthetic data generators.tutorial.ipynb: Jupyter notebook tutorial.
Citation
If you use this code in your work, please cite:
@InProceedings{pmlr-v258-liu25f,
title = {Information-Theoretic Measures on Lattices for Higher-Order Interactions},
author = {Liu, Zhaolu and Barahona, Mauricio and Peach, Robert},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
pages = {2206--2214},
year = {2025},
volume = {258},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v258/liu25f.html}
}
Owner
- Name: Barahona Research - Applied Math - Imperial
- Login: barahona-research-group
- Kind: organization
- Email: m.barahona@imperial.ac.uk
- Website: https://scholar.google.co.uk/citations?user=weulBoAAAAAJ&hl=en
- Repositories: 9
- Profile: https://github.com/barahona-research-group
Research codes developed in the Barahona research group - Department of Mathematics - Imperial College London
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