bnpy : Bayesian nonparametric machine learning for python.

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About

This python module provides code for training popular clustering models on large datasets. We focus on Bayesian nonparametric models based on the Dirichlet process, but also provide parametric counterparts.

bnpy supports the latest online learning algorithms as well as standard offline methods. Our aim is to provide an inference platform that makes it easy for researchers and practitioners to compare models and algorithms.

Supported probabilistic models (aka allocation models)

Mixture models
- FiniteMixtureModel : fixed number of clusters
- DPMixtureModel : infinite number of clusters, via the Dirichlet process
Topic models (aka admixtures models)
- FiniteTopicModel : fixed number of topics. This is Latent Dirichlet allocation.
- HDPTopicModel : infinite number of topics, via the hierarchical Dirichlet process
Hidden Markov models (HMMs)
- FiniteHMM : Markov sequence model with a fixture number of states
- HDPHMM : Markov sequence models with an infinite number of states

Supported data observation models (aka likelihoods)

Multinomial for bag-of-words data
- Mult
Gaussian for real-valued vector data
- Gauss : Full-covariance
- DiagGauss : Diagonal-covariance
- ZeroMeanGauss : Zero-mean, full-covariance
Auto-regressive Gaussian
- AutoRegGauss

Supported learning algorithms:

Expectation-maximization
- EM
Full-dataset variational Bayes
- VB
Memoized variational (can process small minibatches per update step)
- moVB
Stochastic variational (can process small minibatches per update step)
- soVB

These are all variants of variational inference, a family of optimization algorithms.

Example Gallery

You can find many examples of bnpy in action in our curated Example Gallery.

These same demos are also directly available as Python scrips inside the examples/ folder of the project Github repository.

Quick Start

You can use bnpy from a command line/terminal, or from within Python. Both options require specifying a dataset, an allocation model, an observation model (likelihood), and an algorithm. Optional keyword arguments with reasonable defaults allow control of specific model hyperparameters, algorithm parameters, etc.

Below, we show how to call bnpy to train a 8 component Gaussian mixture model on a default toy dataset stored in a .csv file on disk. In both cases, log information is printed to stdout, and all learned model parameters are saved to disk.

Calling from the terminal/command-line

$ python -m bnpy.Run /path/to/my_dataset.csv FiniteMixtureModel Gauss EM --K 8 --output_path /tmp/my_dataset/results/

Calling directly from Python

import bnpy
bnpy.run('/path/to/dataset.csv',
         'FiniteMixtureModel', 'Gauss', 'EM',
         K=8, output_path='/tmp/my_dataset/results/')

Advanced examples

Train Dirichlet-process Gaussian mixture model (DP-GMM) via full-dataset variational algorithm (aka "VB" for variational Bayes).

python -m bnpy.Run /path/to/dataset.csv DPMixtureModel Gauss VB --K 8

Train DP-GMM via memoized variational, with birth and merge moves, with data divided into 10 batches.

python -m bnpy.Run /path/to/dataset.csv DPMixtureModel Gauss memoVB --K 8 --nBatch 10 --moves birth,merge

Quick help

# print help message for required arguments
python -m bnpy.Run --help 

# print help message for specific keyword options for Gaussian mixture models
python -m bnpy.Run /path/to/dataset.csv FiniteMixtureModel Gauss EM --kwhelp

Installation

To use bnpy for the first time, follow the documentation's Installation Instructions.

Team

Primary investigators

Mike Hughes
Assistant Professor (Aug. 2018 - present)
Tufts University, Dept. of Computer Science
Website: https://www.michaelchughes.com

Erik Sudderth Professor
University of California, Irvine
Website: https://www.ics.uci.edu/~sudderth/

Contributors

Soumya Ghosh
Dae Il Kim
Geng Ji
William Stephenson
Sonia Phene
Gabe Hope
Leah Weiner
Alexis Cook
Mert Terzihan
Mengrui Ni
Jincheng Li
Xi Chen (Tufts)

Academic Papers

Conference publications based on BNPy

NIPS 2015 HDP-HMM paper

Our NIPS 2015 paper describes inference algorithms that can add or remove clusters for the sticky HDP-HMM.

"Scalable adaptation of state complexity for nonparametric hidden Markov models." Michael C. Hughes, William Stephenson, and Erik B. Sudderth. NIPS 2015. [paper] [supplement] [scripts to reproduce experiments]

AISTATS 2015 HDP topic model paper

Our AISTATS 2015 paper describes our algorithms for HDP topic models.

"Reliable and scalable variational inference for the hierarchical Dirichlet process." Michael C. Hughes, Dae Il Kim, and Erik B. Sudderth. AISTATS 2015. [paper] [supplement] [bibtex]

NIPS 2013 DP mixtures paper

Our NIPS 2013 paper introduced memoized variational inference algorithm, and applied it to Dirichlet process mixture models.

"Memoized online variational inference for Dirichlet process mixture models." Michael C. Hughes and Erik B. Sudderth. NIPS 2013. [paper] [supplement] [bibtex]

Workshop papers

Our short paper from a workshop at NIPS 2014 describes the vision for bnpy as a general purpose inference engine.

"bnpy: Reliable and scalable variational inference for Bayesian nonparametric models." Michael C. Hughes and Erik B. Sudderth. Probabilistic Programming Workshop at NIPS 2014. [paper]

Target Audience

Primarly, we intend bnpy to be a platform for researchers. By gathering many learning algorithms and popular models in one convenient, modular repository, we hope to make it easier to compare and contrast approaches. We also hope that the modular organization of bnpy enables researchers to try out new modeling ideas without reinventing the wheel.

bnpy/bnpy