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- Title
Embo: a Python package for empirical data analysis using the Information Bottleneck.
- Authors
PIASINI, EUGENIO; FILIPOWICZ, ALEXANDRE L. S.; LEVINE, JONATHAN; GOLD, JOSHUA I.
- Abstract
We present embo, a Python package to analyze empirical data using the Information Bottleneck (IB) method and its variants, such as the Deterministic Information Bottleneck (DIB). Given two random variables X and Y, the IB finds the stochastic mapping M of X that encodes the most information about Y, subject to a constraint on the information that M is allowed to retain about X. Despite the popularity of the IB, an accessible implementation of the reference algorithm oriented towards ease of use on empirical data was missing. Embo is optimized for the common case of discrete, lowdimensional data. Embo is fast, provides a standard data-processing pipeline, offers a parallel implementation of key computational steps, and includes reasonable defaults for the method parameters. Embo is broadly applicable to different problem domains, as it can be employed with any dataset consisting in joint observations of two discrete variables. It is available from the Python Package Index (PyPI), Zenodo and GitLab.
- Subjects
PYTHON programming language; INFORMATION theory; DETERMINISTIC algorithms; DATA analysis; STATISTICS
- Publication
Journal of Open Research Software, 2021, Vol 9, Issue 1, p1
- ISSN
2049-9647
- Publication type
Article
- DOI
10.5334/jors.322