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Embo: a Python package for empirical data analysis using the Information Bottleneck
Eugenio Piasini1, Alexandre L S Filipowicz2, Jonathan Levine3
1Computational Neuroscience Initiative and Department of Physics and Astronomy, University of Pennsylvania.
Journal of Open Research Software
|May 8, 2023
Summary
We introduce embo, a Python package for analyzing data with the Information Bottleneck (IB) method. This tool simplifies complex data analysis for discrete variables, offering speed and ease of use.
Area of Science:
- Machine Learning
- Information Theory
- Computational Statistics
Background:
- The Information Bottleneck (IB) method is a powerful tool for analyzing relationships between variables.
- Existing implementations often lack accessibility for empirical data analysis.
- A need exists for a user-friendly, efficient Python package for IB applications.
Purpose of the Study:
- To introduce embo, a novel Python package designed for Information Bottleneck analysis.
- To provide an accessible and efficient implementation of the IB method and its variants for discrete data.
- To facilitate the application of IB to diverse empirical datasets.
Main Methods:
- Development of the 'embo' Python package.
- Implementation of the Information Bottleneck (IB) and Deterministic Information Bottleneck (DIB) algorithms.
- Optimization for discrete, low-dimensional data with parallel processing capabilities.
Main Results:
- Embo offers a fast and standard data-processing pipeline for IB analysis.
- The package includes optimized defaults for IB method parameters.
- Embo is broadly applicable to datasets with joint observations of two discrete variables.
Conclusions:
- Embo addresses the need for an accessible and efficient Python package for Information Bottleneck analysis.
- The package simplifies the application of IB to empirical data, enhancing its usability in various domains.
- Embo is readily available via PyPI, Zenodo, and GitLab for researchers and practitioners.
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