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README.md

Relation Learning With Minimum Predictive Information Regularization

This repository contains the code for the paper "Discovering Nonlinear Relations with Minimum Predictive Information Regularization" by Wu et al. 2019. It contains the implementation of the proposed Minimum Predictive Information Regularization (MPIR) method and several compared methods, as well as the three experiments of the paper.

Requirements

Install other required packages by:

pip install -r requirements.txt

This repository uses the submodule pytorch_net for easy construction and training of neural networks. Initialize this submodule by:

git submodule init; git submodule update

Learning

The dataset preparation and relation learning with different methods are via the script causality/causality_unified_exp.ipynb (or its corresponding .py file). All datasets are accompanied inside the datasets/ folder or can be directly generated (synthetic dataset). Several methods are provided inside the causality_unified_exp.ipynb script:

  • Our MPIR method
  • Mutual information
  • Transfer Entropy
  • Linear Granger
  • Elastic Net
  • Causal Influence

The result is saved under the data/ folder as a pickle binary file.

Citation

If you compare with, build on, or use aspects of the work, please cite the following:

@article{wu2020discovering,
  title={Discovering Nonlinear Relations with Minimum Predictive Information Regularization},
  author={Wu, Tailin and Breuel, Thomas and Skuhersky, Michael and Kautz, Jan},
  journal={arXiv preprint arXiv:2001.01885},
  year={2020}
}

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Discovering directional relations via minimum predictive information regularization

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