=======
revrand

.. image:: https://travis-ci.org/NICTA/revrand.svg?branch=master
:target: https://travis-ci.org/NICTA/revrand

.. image:: https://codecov.io/github/NICTA/revrand/coverage.svg?branch=master
:target: https://codecov.io/github/NICTA/revrand?branch=master

Note: we are not actively developing this library anymore, but we are still
maintaining it. We recommend instead looking at Aboleth <https://github.com/data61/aboleth>_, which has similar functionality and is
implemented on top of TensorFlow.

---

A library of scalable Bayesian generalized linear models with fancy features

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 <https://github.com/GPflow/GPflow>_ and GPy <https://github.com/SheffieldML/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 <http://scikit-learn.org/stable/modules/pipeline.html>_.
• A host of decorators for scipy.optimize.minimize <https://docs.scipy.org/doc/scipy/reference/optimize.html>_ 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,

.. image:: docs/matern32.png

here is an example of the algorithms in revrand approximating a Gaussian
Process,

.. image:: docs/glm_sgd_demo.png

and here is an example of running using our Bayesian GLM with a Poisson
likelihood and integer observations,

.. image:: docs/glm_demo.png

Have a look at some of the demo notebooks <demos/>_ for how we generated
these plots, and more!

Quickstart

To install, you can use pip:

.. code:: console

$ pip install revrand

or simply run setup.py in the location where you have cloned or
downloaded this repository:

.. code:: console

$ python setup.py install

Now have a look at our quickstart guide <http://nicta.github.io/revrand/quickstart.html>_ to get up and running
quickly!

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 <https://github.com/NICTA/revrand/issues>_.

Authors

• Daniel Steinberg <https://github.com/dsteinberg>_
• Louis Tiao <https://github.com/ltiao>_
• Alistair Reid <https://github.com/AlistaiReid>_
• Lachlan McCalman <https://github.com/lmccalman>_
• Simon O'Callaghan <https://github.com/socallaghan>_

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] 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 & 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.