1. Mallet Extension

In Mallet package, it only contains two topic Models--LDA and Hierachical LDA. So I tried to implement some useful topic modeling methods on it.

Model:

• Hierarchical Dirichlet Process with Gibbs Sampling. (in HDP folder)
• Inference part for hLDA. (in hLDA folder)

Usage:

1. This is an extension for Mallet, so you need to have Mallet's source code first.
2. put HDP.java,HDPInferencer.java and HierarchicalLDAInferencer.java in src/cc/mallet/topics folder.
3. If you are going to run HDP, make sure you include knowceans package in your project.
4. run HDPTest.java or hLDATest.java will give you a demo for a small dataset in data folder.

References:

• Mallet: http://mallet.cs.umass.edu/
• knowceans: http://sourceforge.net/projects/knowceans/
• HDP paper: http://www.cs.berkeley.edu/~jordan/papers/hdp.pdf
• HDP paper & source code: "Implementing the HDP with minimum code complexity" by Gregor Heinrich

2. Scikit-learn Extension

Scikit-learn doesn't have any topic models yet, so I modified Matthew D. Hoffman's onlineldavb into scikit-learn format.

Model:

• online LDA with variational EM. (In LDA folder)

Usage:

1. Make sure numpy, scipy, and scikit-learn are installed.
2. run python test in lda folder for unit test
3. The onlineLDA model is in lda.py.
4. For a quick exmaple, run python lda_example.py online will fit a 10 topics model with 20 NewsGroup dataset. online means we use online update(or partial_fit method). Change online to batch will fit the model with batch update(or fit method).

Note:
I updated the currecnt code based on currrent scikit-learn development branch. Older scikit-learn version will thorw error, but I am pretty sure everyone can fixed this with little alteration. (2014/09/15)

Reference:

• Scikit-learn: http://scikit-learn.org
• onlineLDA: http://www.cs.princeton.edu/~mdhoffma/code/onlineldavb.tar
• online LDA paper: http://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf