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Dirichlet

A Python package to estimate the Dirichlet distribution, calculate maximum likelihood, and test for independence from a variable based on fitting nested Dirichlet distribution hypotheses.

Most of this package is a port of Thomas P. Minka's wonderful Fastfit MATLAB code. Much thanks to him for that and his clear paper "Estimating a Dirichlet distribution".

Dirichlet Test

This likelihood ratio test for independence will determine whether two Dirichlet-distributed data sets are likely to be from the same distribution or from two different ones, much like a chi-square or G-test for independence, but with Dirichlet models.

Simplex Plots

The dirichlet.simplex module creates scatter, contour, and filled contour 2-simplex plots.

Caveats

Note that this package at the moment doesn't support sparse data vectors due to the numerical fitting algorithm that uses the gamma function. Possibly some sort of additive smoothing would make this package work in your context, but that will depend on your application.

Installation

pip install git+https://github.com/ericsuh/dirichlet.git

This has only been tested with Python 3.6+. Other versions may work, but they haven't been tested.

Development

Note: These instructions have only been tested on Ubuntu/Debian.

Dev dependencies are listed in requirements-dev.txt. You can install them with:

pip install -r requirements-dev.txt

Code style

Please use black to format your code when contributing

Testing

This project uses tox and pytest for testing. To run tests, generally you can just run:

tox

To test a particular version of Python, you will need to have it installed and in your $PATH ahead of time.