https://github.com/kzhai/PyCTM

A Correlated Topic Model implementation in Python.
https://github.com/kzhai/PyCTM

Last synced: about 2 months ago
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A Correlated Topic Model implementation in Python.

• Host: GitHub
• URL: https://github.com/kzhai/PyCTM
• Owner: kzhai
• Created: 2016-08-26T06:44:10.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2020-04-24T00:13:26.000Z (over 4 years ago)
• Last Synced: 2024-05-15T04:40:03.475Z (4 months ago)
• Language: Python
• Size: 7.37 MB
• Stars: 31
• Watchers: 3
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
PyCTM

==========

PyCTM is a Correlated Topic Modeling package, please download the latest version from our [GitHub repository](https://github.com/kzhai/PyCTM).

Please send any bugs of problems to Ke Zhai ([email protected]).

Install and Build

----------

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

Launch and Execute

----------

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

	$PROJECT_SPACE/src/PyCTM

To prepare the example dataset,

	tar zxvf pnas-abstract.tar.gz

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

	cd $PROJECT_SPACE/src/PyCTM

and run the following command on example dataset,

	python -m launch_train --input_directory=./pnas-abstract --output_directory=./ --number_of_topics=10 --training_iterations=50

The generic argument to run PyCTM 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