SMOOTHED GRADIENT FOR STOCHASTIC VARIATIONAL INFERENCE
Hojae Choi hojae.choi@kaist.ac.kr
(C) Copyright 2016, Hojae Choi
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 smoothed gradient (SG) for stochastic variational inference (SVI) in latent dirichlet allocation (LDA) model presented in the paper "Smoothed Gradients for Stochastic Variational Inference", Mandt S. and Blei D, NIPS 2014.
This code is based on the project of blei-lab/onlineldavb (https://github.com/blei-lab/onlineldavb)
File provided:
- batchldavb.py : A package functions for fitting LDA using stochastic optimization with smoothed gradient
- calculate_prop.py : A script for calculate mean squared bias (MSB), variance, mean squared error (MSE).
- wikiabs.py : A script for measure predictive probability in iterations to validate hyper parameter (L) of smothed gradient
- dictnostops.txt : A vocabulary of English words with the stop words removed.
- README.md : This file
- corpus.py : A package functions to read corpus.
- parsexml.py : A script to parse contencts and extract word-count from XML file into corpus format.
You will need to have the numpy and scipy packages installed somewhere
that Python can find them to use these scripts.
And you also need to have the multiprocessing and threading modules
which are used in wikiabs.py to validate models
Example 1
python batchldavb.py [path to corpus] 100 0.5 0.5 -1 200 300 [path to vocab]
This will set LDA model and stochastic optimizer, K = 100, alpha = 0.5, eta = 0.5, kappa = -1, windowsize(L) = 200, minibach size = 300; and store the gradients matrix at some iterations in "gradient-[L]" folder.
Example 2
python wikiabs.py 30
This will set parameters same as case of the paper (Mandt S., 2014). And measuring predictive probabilities for L = 30 per 300 iterations
Example 3
python calculate_prop.py
This will calculate MSB, variance and MSE using gradient get from "Example 1"
This code was implemented during final project of 2016 spring EE531 : Statistical Learning Theory, KAIST
Advisor : Changdong Yoo
Team members : Hojae Choi, Soorin Yim, Yunwon Kang
Soorin Yim, Yunwon Kang help me understand the fundamentals and theoretical base of variational inference, stochastic optimization and latent dirichlet allocation.