Collaborative Topic Modeling for Recommending Github Repos

What is GitHub LDA?

GitHub LDA is a library that applies topic modeling on GitHub repos to improve repository recommendation.

Usage

Download the GitHub Contest dataset

wget https://github.s3.amazonaws.com/data/download.zip
unzip download.zip

Clone the git repositories from GitHub

mkdir repo_dir
github_lda clone -i download/repos.txt -o repo_dir [-p 4]

As there are around 120,000 repositories to download, this will take a VERY long time and will eat up a huge chunk of disk space (up to 1TB). You can specify the number of clones to run in parallel by using the -p option. In order to avoid the number of directories limit in *nix, by default it will subdivide the repositories into 13 subdirectories as follows:

repo_dir
|---0
|   |---1
|   |---2
|   |---...
|   `---9999
|---1
|   |---10000
|   |---10001
|   |---...
.
|   `---119999
`---12
    |---120000
    |---120001
    |---...
    `---123344

Calculate the term frequency for each repository

mkdir term_freq_dir
github_lda calctf -i repo_dir -o term_freq_dir [--stopwords=/path/to/stopwords] [--lang=ruby,javascript] [--process=1]

You can limit the repositories of interest by using the --lang option. By default, term frequencies for source files of all programming languages will be calculated. Refer here for the list of available language options. You can also specify the number of processors to run on by using the --process option.

Preprocess the corpus and convert it into lda-c format and ctr format data

Generate mult.dat, user.dat, item.dat, and vocab.dat in specified directory

mkdir data
github_lda generate --tf term_freq_dir -i download/data.txt -o data

Run lda-c-dist

mkdir lda-result
lda est 0.1 100 settings.txt data/mult.dat random lda-result

Run ctr

ctr --user data/user.dat --item data/item.dat --mult mult.dat \
  --theta_init lda-result/final.gamma --beta_init lda-result/final.beta

Resources

• Wikipedia article on LDA
• Official GitHub Blog post: The 2009 GitHub Contest
• Official GitHub Blog post: About the GitHub Contest

References

Chong Wang and David M. Blei. 2011. Collaborative Topic Modeling for Recommending Scientific Articles. In Proc of KDD'11 [pdf].