PyLLDA is a labelled Latent Dirichlet Allocation topic modeling package.

Please download the latest version from our GitHub repository.

Please send any bugs or problems to Ke Zhai (kzhai@umd.edu).

This package depends on many external python libraries, such as numpy, scipy and nltk.

Assume the PyLLDA package is downloaded under directory $PROJECT_SPACE/src/, i.e.,

$PROJECT_SPACE/src/PyLLDA

To prepare the example dataset,

tar zxvf review.4class.stem.tar.gz

To launch PyLLDA, first redirect to the directory of PyLLDA source code,

cd $PROJECT_SPACE/src/PyLLDA

and run the following command on example dataset,

python -m launch_train --input_directory=./review.4class.stem/ --output_directory=./ --training_iterations=50

The generic argument to run PyLLDA is

python -m launch_train --input_directory=$INPUT_DIRECTORY/$CORPUS_NAME --output_directory=$OUTPUT_DIRECTORY --training_iterations=$NUMBER_OF_ITERATIONS

You should be able to find the output at directory $OUTPUT_DIRECTORY/$CORPUS_NAME.

Under any cirsumstances, you may also get help information and usage hints by running the following command

python -m launch_train --help