PyLDA is a Latent Dirichlet Allocation topic modeling package, developed by the Cloud Computing Research Team in University of Maryland, College Park.
Please download the latest version from our GitHub repository.
Please send any bugs of problems to Ke Zhai ([email protected]).
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