IOBHTMM

This repository provides the official Python implementation for the Bottom-up Hidden Tree Markov model (BHTMM) in its (more general) input-output version (IOBHTMM). The model learns a distribution over tree-structured data, implemented throughout a generative process acting from the leaves to the root of the tree.

The library includes both a script to reproduce the tree classification experiments reported in the paper describing the method, as well as a more general configuration script showing how to use the model both in homogenous (BHTMM) and input-driven (IOBHTMM) version.

This research software is provided as is. If you happen to use or modify this code, please remember to cite the foundation papers:

Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti, Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees. IEEE Transactions on Neural Networks and Learning Systems, 23 (12), pp. 1987-2002, 2012

Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti, An input–output hidden Markov model for tree transductions. Neurocomputing, 112, pp. 34–46, 2013

If you have any query concerning the model (not its implementation), feel free to contact the corresponding Author of the paper (http://www.di.unipi.it/~bacciu/). Note that the code can be easily adapted to compute the generative Jaccard tree kernels described here:

Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti, Integrating bi-directional contexts in a generative kernel for trees. Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 4145-4151, 2014