mixmarkov

Reproducibility package for the paper:

Lucas Maystre, Tiffany Wu, Roberto Sanchis Ojeda, Tony Jebara. Multistate Analysis with Infinite Mixtures of Markov Chains, UAI 2022.

This repository contains

• a reference implementation of the algorithms presented in the paper, and
• Jupyter notebooks enabling the reproduction of some of the experiments.

The paper and the library address the problem of predicting trajectories over a small number of states. The main goal is to estimate a model that makes accurate and calibrated probabilistic predictions about states at future points in time, given a sequence's past.

Getting started

To get started, follow these steps:

• Clone the repo locally with: git clone https://github.com/spotify-research/mixmarkov.git
• Move to the repository: cd mixmarkov
• Install the dependencies: pip install -r requirements.txt
• Install the package: pip install -e lib/
• Move to the notebook folder: cd notebooks
• Start a notebook server: jupyter notebok

Our codebase was tested with Python 3.8. The following libraries are required (and installed automatically via the first pip command above):

• numpy (tested with version 1.22.4)
• scipy (tested with version 1.8.1)
• matplotlib (tested with version 3.5.2)
• networkx (tested with version 2.8.3)
• jax (tested with version 0.3.13)
• notebook (tested with version 6.4.11)

Support

Create a new issue

Contributing

We feel that a welcoming community is important and we ask that you follow Spotify's Open Source Code of Conduct in all interactions with the community.

Author

Lucas Maystre

A full list of contributors can be found on GitHub.

Follow @SpotifyResearch on Twitter for updates.

License

Copyright 2022 Spotify AB.

Licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0

Security Issues?

Please report sensitive security issues via Spotify's bug-bounty program (https://hackerone.com/spotify) rather than GitHub.