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

The Gibbs sampler was built from that the Cython implementation here: https://github.com/lda-project/lda. Current Gibbs sampler supports asymmetric priors.

sh build.sh

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
python test_synth.py

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.

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