About

This repository contains the implementation that performed the experiments for the research paper:

• Steven Holtzen, Todd Millstein, and Guy Van den Broeck. "Generating and Sampling Orbits for Lifted Probabilistic Inference." Uncertainty in Artificial Intelligence (UAI). 2019.

To cite, please use:

@inproceedings{HoltzenUAI19,
  author    = {Holtzen, Steven and Millstein, Todd and Van den Broeck, Guy},
  title     = {Generating and Sampling Orbits for Lifted Probabilistic Inference},
  booktitle = {Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI)},
  month     = {jul},
  year      = {2019},
}

Disclaimer

This code is provided as-is. It is what was used to run the experiments in the paper. It is typical research-ware; there are many improvements that could be made, and it is definitely not ready for prime-time (i.e., production). Think of it like extra-detailed documentation on how the algorithm described in the paper works.

Installation

While it may look like regular Python, this code must actually be run using the Sage math library. To use this library:

1. Install sage.
2. Install bliss by running sage -i bliss (if you want to hack the code to remove this requirement, it is possible to do so, but it will be much slower)
3. Run sage setup.py

Organization

• factor.py contains the most interesting code. It has a simple implementation of a factor graph and the exact and approximate lifted inference algorithms from the paper.
• my_graphs.py contains the example factor graphs that are generated
• experiments.py contains stubs for running the experiments from the paper
• test.py contains some standard test cases that illustrate usage.

To execute these files, use sage (not the regular Python command). I.e., run sage test.py.