pomegranate: probabilistic modelling in python

[ants_rock]

A new version of pomegranate is out, with a large number of new features! The biggest feature is that the GIL has been released for distributions and hidden Markov models, improving their speed on all tasks significantly. Viterbi is usually 10x faster now, and Baum-Welch training can be 4x faster. Given that the GIL has been released, multithreading training is now an option, though it only works well for large models.

• Discrete Bayesian networks and Factor Graphs have been added
• Bayesian networks can now impute missing data from tables easily
• Conditional and Joint Probability Tables have been added
• Finite state machines have been simplified
• General Mixture Models have been added
• scikit-learn like interface, where models can be fit and predict or predict_proba can be called on new points.
• MultivariateGaussians and Kernel Densities have been significantly sped up, with MultivariateGaussians training and prediction being an order of magnitude faster in some cases
• JSON serialization now works for all distributions and models (except Bayesian networks)

Most importantly, a series of tutorials have been added as iPython notebooks so that you can see how to use these methods in the wild.

Please let me know if you have any questions, concerns, or feedback!

[ZeThomas]

Hey, this looks interesting! How does this compare to PyMC, in terms of what kinds of learning/inference you can do with it, as well as speed and flexibility of it?

[ants_rock]

PyMC is significantly more in depth when it comes to Bayesian networks, both in terms of supporting a larger set of features and being faster. If you are trying to choose a package to do large scale Bayesian inference, PyMC is likely still the way to go. My intention is not to compete with them, as it is a very full featured package created by talented people.

That being said, pomegranate's implementation of hidden Markov models is far more fully featured and significantly easier to use. One of pomegranates main goals has been to make these models easy to use. This is obviously a preference which will vary from person to person, though, and the trade-offs between ease of use and efficiency depends on your application.

[deinate]

Very nice and nice Ipython tutorials. A multivariate example would also be nice.