Parallel Semi-Supervised Latent Dirichlet Allocation
Switch branches/tags
Nothing to show
Clone or download
David Andrzejewski
Latest commit 6f564cd Apr 5, 2011


Version 0.1

David Andrzejewski (
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"

The implementation consists of Python extension modules written in C
and Cython.


Building this module requires Python, NumPy, Cython, and a C compiler.
From the command-line, do:

% python install

(Note that if things are installed to non-standard locations, you may
need to make the appropriate changes in

There is a simple example scipt showing how to use pSSLDA:

% python example/


If you do not have write access to your Python installation directories,
you will need to tell to install this module somewhere else.
For example:

% python install --prefix=~/local

will install the module under a subdirectory of your home directory called 

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 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


[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