passglm: a package for creating and evaluating PASS-GLM models
passglm package was used produce the experiments for:
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick. PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference . In Proc. of the 31st Annual Conference on Neural Information Processing Systems (NIPS), 2017.
The package includes functionality to load data, construct PASS-GLM approximations for logistic regression, run an adaptive Metropolis-Hastings sampler, and compare performance of PASS-GLM inferences to those obtained with other methods. Support for streaming and distributed inference is included.
Compilation and testing
To compile and test the package (for development purposes):
python setup.py build_ext --inplace # compile cython code in place nosetests tests/ # run tests, which takes a minute or so
pip install .
For example usages, see the scripts/ directory.