py-factorgraph

This is a tiny python library that allows you to build factor graphs and run the (loopy) belief propagation algorithm with ease. It depends only on numpy.

Installation

pip install factorgraph

Example

Code (found in examples/simplegraph.py):

import numpy as np
import factorgraph as fg

# Make an empty graph
g = fg.Graph()

# Add some discrete random variables (RVs)
g.rv('a', 2)
g.rv('b', 3)

# Add some factors, unary and binary
g.factor(['a'], potential=np.array([0.3, 0.7]))
g.factor(['b', 'a'], potential=np.array([
        [0.2, 0.8],
        [0.4, 0.6],
        [0.1, 0.9],
]))

# Run (loopy) belief propagation (LBP)
iters, converged = g.lbp(normalize=True)
print 'LBP ran for %d iterations. Converged = %r' % (iters, converged)
print

# Print out the final messages from LBP
g.print_messages()
print

# Print out the final marginals
g.print_rv_marginals()

Output:

LBP ran for 3 iterations. Converged = True

Current outgoing messages:
	b -> f(b, a) 	[ 0.33333333  0.33333333  0.33333333]
	f(a) -> a 	[ 0.3  0.7]
	a -> f(a) 	[ 0.23333333  0.76666667]
	a -> f(b, a) 	[ 0.3  0.7]
	f(b, a) -> b 	[ 0.34065934  0.2967033   0.36263736]
	f(b, a) -> a 	[ 0.23333333  0.76666667]

Marginals for RVs:
a
	0 	0.07
	1 	0.536666666667
b
	0 	0.340659340659
	1 	0.296703296703
	2 	0.362637362637

Visualization

You can use factorgraph-viz to visualize factor graphs interactively in your web browser.

Projects using `py-factorgraph`

Open an issue or send a PR if you'd like your project listed here.

verbphysics

Contributing

There's plenty of low-hanging fruit to work on if you'd like to contribute to this project. Here are some ideas:

Unit tests
Auto-generated python docs (what's popular these days?)
python3 compatability
Performance: measure bottlenecks and improve them (ideas: numba; parallelization for large graphs;)
Remove or improve ctrl-C catching (the E_STOP)
Cleaning up the API (essentially duplicate constructors for RVs and Factors within the Graph code; probably should have a node superclass for RVs and Factors that pulls out common code).

Thanks

to Matthew R. Gormley and Jason Eisner for the Structured Belief Propagation for NLP Tutorial, which was extremely helpful for me in learning about factor graphs and understanding the sum product algorithm.
to Ryan Lester for pyfac, whose tests I used directly to test my implementation

manuelschmidt/py-factorgraph