https://github.com/nicta/revrand
A library of scalable Bayesian generalised linear models with fancy features
https://github.com/nicta/revrand
bayesian-inference fourier-features gaussian-processes generalized-linear-models regression
Last synced: 6 months ago
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A library of scalable Bayesian generalised linear models with fancy features
- Host: GitHub
- URL: https://github.com/nicta/revrand
- Owner: NICTA
- License: apache-2.0
- Created: 2015-05-18T02:29:52.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2017-09-07T04:29:27.000Z (almost 9 years ago)
- Last Synced: 2025-12-05T14:42:39.285Z (7 months ago)
- Topics: bayesian-inference, fourier-features, gaussian-processes, generalized-linear-models, regression
- Language: Python
- Homepage:
- Size: 82.9 MB
- Stars: 60
- Watchers: 18
- Forks: 14
- Open Issues: 8
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
=======
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
`_, 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 `_ 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,
.. 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 `_ 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
`_ 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 `_.
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] 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.