revrand 0.5.1

A library of scalable Bayesian generalised linear models with fancy features

A library of scalable Bayesian generalised linear models with fancy features

This library implements various Bayesian linear models (Bayesian linear regression) and generalised linear models. A few features of this library are:

• A fancy basis functions/feature composition framework for combining basis functions like radial basis function, sigmoidal basis functions, polynomial basis functions etc.

• Basis functions that can be used to approximate Gaussian processes with shift invariant covariance functions (e.g. square exponential) when used with linear models [1], [2], [3].

• Non-Gaussian likelihoods with Bayesian generalised linear models using a modified version of the nonparametric variational inference algorithm presented in [4].

• Large scale learning using stochastic gradient descent (Adam, AdaDelta and more).

• Scikit Learn compatibility, i.e. usable with pipelines.

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!

Quickstart

To install, simply run setup.py:

$ python setup.py install

or install with pip:

$ pip install git+https://github.com/nicta/revrand.git

Now have a look at our quickstart guide to get up and running quickly!

Refer to docs/installation.rst for advanced installation instructions.

Useful Links

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

Bugs & Feedback

For bugs, questions and discussions, please use Github Issues.

Authors

• Daniel Steinberg

• Louis Tiao

• Alistair Reid

• Lachlan McCalman

• Simon O’Callaghan

References

[1]

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]

Le, Q., Sarlos, T., & Smola, A. “Fastfood-approximating kernel expansions in loglinear time.” Proceedings of the international conference on machine learning. 2013.

[3]

Rahimi, A., & Recht, B. “Random features for large-scale kernel machines.” Advances in neural information processing systems. 2007.

[4]

Gershman, S., Hoffman, M., & Blei, D. “Nonparametric variational inference”. arXiv preprint arXiv:1206.4665 (2012).

Copyright & License

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

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.