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 ([email protected]).
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