David Andrzejewski (andrzeje@cs.wisc.edu) Department of Computer Sciences University of Wisconsin-Madison, USA
This software implements an extension of Latent Dirichlet Allocation (LDA) [2] which includes "topic-in-set knowledge", or z-labels [1], allowing the user to supply (possibly noisy) labels for specific latent topic assignments. Parallelized inference is done by the Approximate Distributed (AD) [3] collapsed Gibbs sampling algorithm.
This code can also be used to do parallel inference for "standard" LDA.
The implementation consists of Python extension modules written in C and Cython. Hannah Devinney (hdevinney) migrated the code to be Python3-compatible.
Building this module requires Python, NumPy, Cython, and a C compiler. From the command-line, do:
% python setup.py install
(Note that if things are installed to non-standard locations, you may need to make the appropriate changes in setup.py)
There is a simple example scipt showing how to use pSSLDA:
% python example/example_py3.py
If you do not have write access to your Python installation directories, you will need to tell setup.py to install this module somewhere else. For example:
% python setup.py install --prefix=~/local
will install the module under a subdirectory of your home directory called "local".
It may then be necessary to let Python know where that is by setting
the PYTHONPATH environment variable (e.g., in .bashrc or .cshrc). For
our example this might involve adding something like the line:
setenv PYTHONPATH ~/local/lib/python2.5/site-packages
The commenting in the example.py script explains the meanings and types of all input and return arguments. The P parameter determines how many parallel sampling processes to run - using a value larger than the number of available cores is probably inadvisable.
This software is open-source, released under the terms of the GNU General Public License version 3, or any later version of the GPL (see LICENSE).
[1] Andrzejewski, D. and Zhu, X. (2009). Latent Dirichlet Allocation with Topic-in-Set Knowledge. NAACL 2009 Workshop on Semi-supervised Learning for NLP (NAACL-SSLNLP 2009)
[2] Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research (JMLR) 3 (Mar. 2003), 993-1022.
[3] Newman, D., Asuncion, A., Smyth, P., and Welling, M. Distributed Algorithms for Topic Models. Journal of Machine Learning Research (JMLR) 10 (Aug. 2009), 1801-1828.
0.1 Initial release