Note: we are not actively developing this library anymore, but we are still maintaining it. We recommend instead looking at Aboleth, which has similar functionality and is implemented on top of TensorFlow.
revrand is a python (2 and 3) supervised machine learning library that contains implementations of various Bayesian linear and generalized linear models (i.e. Bayesian linear regression and Bayesian generalized linear regression).
revrand can be used for large scale approximate Gaussian process regression, like GPflow and GPy, but it uses random basis kernel approximations (see [1], [2], [3]) as opposed to inducing point approximations.
A few features of this library are:
- Random Basis functions that can be used to approximate Gaussian processes with shift invariant covariance functions (e.g. Matern) when used with linear models [1], [2], [3].
- A fancy basis functions/feature composition framework for combining basis functions like those above and radial basis functions, sigmoidal basis functions, polynomial basis functions etc with basis function parameter learning.
- Non-Gaussian likelihoods with Bayesian generalized linear models (GLMs). We infer all of the parameters in the GLMs using stochastic variational inference [4], and we approximate the posterior over the weights with a mixture of Gaussians, like [5].
- Large scale learning using stochastic gradients (Adam, AdaDelta and more).
- Scikit Learn compatibility, i.e. usable with pipelines.
- A host of decorators for scipy.optimize.minimize and stochastic gradients that enhance the functionality of these optimisers.
Here is an example of approximating a Matern 3/2 kernel with some of our basis functions,
here is an example of the algorithms in revrand approximating a Gaussian Process,
and here is an example of running using our Bayesian GLM with a Poisson likelihood and integer observations,
Have a look at some of the demo notebooks for how we generated these plots, and more!
To install, you can use pip:
$ pip install revrandor simply run setup.py in the location where you have cloned or
downloaded this repository:
$ python setup.py installNow have a look at our quickstart guide to get up and running quickly!
- Home Page
- http://github.com/nicta/revrand
- Documentation
- http://nicta.github.io/revrand
- Report on the algorithms in revrand
- https://github.com/NICTA/revrand/blob/master/docs/report/report.pdf
- Issue tracking
- https://github.com/nicta/revrand/issues
For bugs, questions and discussions, please use Github Issues.
| [1] | (1, 2) Yang, Z., Smola, A. J., Song, L., & Wilson, A. G. "A la Carte -- Learning Fast Kernels". Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pp. 1098-1106, 2015. |
| [2] | (1, 2) Le, Q., Sarlos, T., & Smola, A. "Fastfood-approximating kernel expansions in loglinear time." Proceedings of the international conference on machine learning. 2013. |
| [3] | (1, 2) Rahimi, A., & Recht, B. "Random features for large-scale kernel machines". Advances in neural information processing systems. 2007. |
| [4] | Kingma, D. P., & Welling, M. "Auto-encoding variational Bayes". Proceedings of the 2nd International Conference on Learning Representations (ICLR). 2014. |
| [5] | Gershman, S., Hoffman, M., & Blei, D. "Nonparametric variational inference". Proceedings of the international conference on machine learning. 2012. |
Copyright 2015 National ICT Australia.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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