PyLDA is a Latent Dirichlet Allocation topic modeling package, developed by the Cloud Computing Research Team in [University of Maryland, College Park] (http://www.umd.edu).
Please download the latest version from our GitHub repository.
Please send any bugs of problems to Ke Zhai (kzhai@umd.edu).
This package depends on many external python libraries, such as numpy, scipy and nltk.
Assume the PyLDA package is downloaded under directory $PROJECT_SPACE/src/, i.e.,
$PROJECT_SPACE/src/PyLDA
To prepare the example dataset,
tar zxvf associated-press.tar.gz
To launch PyLDA, first redirect to the directory of PyLDA source code,
cd $PROJECT_SPACE/src/PyLDA
and run the following command on example dataset,
python -m launch_train --input_directory=./associated-press --output_directory=./ --number_of_topics=10 --training_iterations=100
The generic argument to run PyLDA is
python -m launch_train --input_directory=$INPUT_DIRECTORY/$CORPUS_NAME --output_directory=$OUTPUT_DIRECTORY --number_of_topics=$NUMBER_OF_TOPICS --training_iterations=$NUMBER_OF_ITERATIONS
You should be able to find the output at directory $OUTPUT_DIRECTORY/$CORPUS_NAME.
Under any circumstances, you may also get help information and usage hints by running the following command
python -m launch_train --help