search

kzhai / PyLDA

A Latent Dirichlet Allocation implementation in Python.
50 stars 22 forks source link
bayesian-inference gibbs-sampler gibbs-sampling latent-dirichlet-allocation lda machine-learning machine-learning-algorithms natural-language-processing nlp python topic-modeling topic-models variational-inference

readme

PyLDA

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 (kzhai@umd.edu).

Install and Build

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

Launch and Execute

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