horizont: Topic models in Python
horizont implements a number of topic models. Conventions from scikit-learn are followed.
The following models are implemented using Gibbs sampling.
Latent Dirichlet allocation (Blei et al., 2003; Pritchard et al., 2000)
(Coming soon) Logistic normal topic model
(Coming soon) Dynamic topic model (Blei and Lafferty, 2006)
Getting started
horizont.LDA implements latent Dirichlet allocation (LDA) using Gibbs sampling. The interface follows conventions in scikit-learn.
>>> import numpy as np
>>> from horizont import LDA
>>> X = np.array([[1,1], [2, 1], [3, 1], [4, 1], [5, 8], [6, 1]])
>>> model = LDA(n_topics=2, random_state=0, n_iter=100)
>>> doc_topic = model.fit_transform(X) # estimate of document-topic distributions
>>> model.components_ # estimate of topic-word distributionsRequirements
Python 2.7 or Python 3.3+ is required. The following packages are also required:
futures (Python 2.7 only)
GSL is required for random number generation inside the Pólya-Gamma random variate generator. On Debian-based sytems, GSL may be installed with the command sudo apt-get install libgsl0-dev. horizont looks for GSL headers and libraries in /usr/include and /usr/lib/ respectively.
Cython is needed if compiling from source.
Important links
Documentation: http://pythonhosted.org/horizont
Source code: https://github.com/ariddell/horizont/
Issue tracker: https://github.com/ariddell/horizont/issues
License
horizont is licensed under Version 3.0 of the GNU General Public License. See LICENSE file for a text of the license or visit http://www.gnu.org/copyleft/gpl.html.
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Release files for horizont 0.0.5
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