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Implementation of Deep Dirichlet Multinomial Regression in python + cython.
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Implementation of Deep Dirichlet Multinomial Regression in Python 3 with Cython Gibbs sampler.

The Gibbs sampler was built from that the Cython implementation here: Current Gibbs sampler supports asymmetric priors.




After building, run the sample code to fit LDA, DMR, and dDMR on synthetic data (data are generated from a deep DMR model):

cd deepdmr

Synthetic data can be found under test_output/data/, model runs can be found under test_output/runs/, and plots under test_output/plots/. The synthetic data is saved in compressed numpy format -- format can be grokked by inspecting this file. Relevant fields are:

  • Ds_body: integer array for document indices
  • Ws_body: integer array for token indices
  • token_dict: dictionary mapping token type to index
  • annotation_names: list of different annotation names associated with each document
  • annotation_dicts: maps annotation name to an index-to-feature name dictionary
  • annotations: dictionary of model parameters along with document supervision. In the synthetic data, the document-level supervision is under the "descriptor" field.

Contact Information

If you use this code and find it useful, please consider citing the accompanying paper:

Adrian Benton and Mark Dredze. Deep Dirichlet Multinomial Regression. 2018. HLT-NAACL.

Feel free to email any questions or concerns to the first author:

first_name dot last_name at gmail dot com

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