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)
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Author
A full list of contributors can be found on GitHub.
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License
Copyright 2022 Spotify AB.
Licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0
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