https://github.com/blei-lab/onlineldavb

Online variational Bayes for latent Dirichlet allocation (LDA)
https://github.com/blei-lab/onlineldavb

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Online variational Bayes for latent Dirichlet allocation (LDA)

• Host: GitHub
• URL: https://github.com/blei-lab/onlineldavb
• Owner: blei-lab
• License: gpl-3.0
• Created: 2014-10-31T20:06:27.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2021-05-21T15:20:53.000Z (over 3 years ago)
• Last Synced: 2024-06-28T06:56:06.402Z (4 months ago)
• Language: Python
• Size: 49.8 KB
• Stars: 297
• Watchers: 51
• Forks: 105
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: COPYING

Awesome Lists containing this project

• awesome-topic-models - onlineldavb - Python online variational Bayes implementation by Matthew Hoffman [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf) (Research Implementations / Embedding based Topic Models)

README

        
ONLINE VARIATIONAL BAYES FOR LATENT DIRICHLET ALLOCATION

Matthew D. Hoffman

[email protected]

(C) Copyright 2010, Matthew D. Hoffman

This is free software, you can redistribute it and/or modify it under

the terms of the GNU General Public License.

The GNU General Public License does not permit this software to be

redistributed in proprietary programs.

This software is distributed in the hope that it will be useful, but

WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

You should have received a copy of the GNU General Public License

along with this program; if not, write to the Free Software

Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307

USA

------------------------------------------------------------------------

This Python code implements the online Variational Bayes (VB)

algorithm presented in the paper "Online Learning for Latent Dirichlet

Allocation" by Matthew D. Hoffman, David M. Blei, and Francis Bach,

to be presented at NIPS 2010.

The algorithm uses stochastic optimization to maximize the variational

objective function for the Latent Dirichlet Allocation (LDA) topic model.

It only looks at a subset of the total corpus of documents each

iteration, and thereby is able to find a locally optimal setting of

the variational posterior over the topics more quickly than a batch

VB algorithm could for large corpora.

Files provided:

* onlineldavb.py: A package of functions for fitting LDA using stochastic

    optimization.

* onlinewikipedia.py: An example Python script that uses the functions in

    onlineldavb.py to fit a set of topics to the documents in Wikipedia.

* wikirandom.py: A package of functions for downloading randomly chosen

    Wikipedia articles.

* printtopics.py: A Python script that displays the topics fit using the

    functions in onlineldavb.py.

* dictnostops.txt: A vocabulary of English words with the stop words removed.

* readme.txt: This file.

* COPYING: A copy of the GNU public license version 3.

You will need to have the numpy and scipy packages installed somewhere

that Python can find them to use these scripts.

Example:

python onlinewikipedia.py 101

python printtopics.py dictnostops.txt lambda-100.dat

This would run the algorithm for 101 iterations, and display the

(expected value under the variational posterior of the) topics fit by

the algorithm. (Note that the algorithm will not have fully converged

after 101 iterations---this is just to give an idea of how to use the

code.)