symfer

symfer is a tool suite, written mostly in Python, for performing probabilistic inference: calculating arbitrary probabilities like , given a model and observation . A script for this specific query is constructed during a symbolic phase in Python, and executed in a separate numeric phase (possibly on a different platform/machine).

Basic usage

From the Python prompt, load a Bayesian network model (stored in Hugin format):

>>> import symfer as s
>>> model = s.loadhugin('extended-student.net')

Select a query variable and set observation :

>>> query = ['H']
>>> obs = {'S':'s1'}

Symbolic phase: construct an inference script (here using variable elimination with minweight heuristic).

>>> ve = s.ve_minweight(model,query)
>>> ve_obs = s.indextree(obs,ve)

Note

It is easy to tweak the supplied Inference algorithms or even roll your own.

Numeric phase: Execute the script, here using the internal evaluator.

>>> s.evaluate(ve_obs)
Multinom(dom=[{'H': ['h0', 'h1']}],par=[0.0677442825, 0.20725571750000002])

This result represents the distribution .

Note

Instead of directly executing the script from the same Python interpreter, it can also be evaluated at a different machine and/or using Java/C. See Evaluation options.

Download

Source distribution:

• symfer-0.2.1.tar.gz
• symfer-0.2.1.zip

See Installing for other options and further instructions.

Tutorial

• Installing
  • Installing NumPy
  • Installing symfer itself
  • C evaluator on Windows
  • Advanced
• Probabilistic models in symfer
• Factor algebra
  • Basic definitions
  • Operators on factors
  • Equalities
  • Factor algebra to Factor
• Inference algorithms
• Evaluation options
• YAML representation

Reference

• Index
• Module Index

License

symfer is licensed under the 2-clause BSD license.

If you use it in published research, please refer to the following article: [pdf]

Sander Evers and Peter J.F. Lucas, A framework for development, teaching and deployment of inference algorithms. In: Andrés Cano, Manuel Gómez-Olmedo, Thomas D. Nielsen (Eds.), PGM 2012, Sixth European workshop on Probabilistic Graphical Models, Electronic proceedings, pp. 99–106, http://leo.ugr.es/pgm2012/proceedings/proceedings.pdf

Development

Currently, symfer has only one developer: Sander Evers. However, he is happy to accept any contributions, or team up with you if you’d like to share some responsibility. Reach him at sandr {at} dds {dot} nl.

Acknowledgements

This software has evolved from a research tool developed at the University of Twente (DB group) and Radboud University Nijmegen (MBSD group).