Merge pull request #78 from GPflow/r0.1

r0.1.0

Author: javdrher

CONTRIBUTING.md

@@ -28,11 +28,11 @@ GPflowOpt currently tries to keep up with the GPflow master, though at some poin
 Changing the minimum required version of TensorFlow that we're compatible with requires a few tasks:
  - update versions in `setup.py`
  - update versions used on travis via `.travis.yml`
- - update version ussed by readthedocs.org via `docsrequire.txt`
+ - update version used by readthedocs.org via `docsrequire.txt`
  - Increment the GPflowOpt version (see below). 
 
 ## Version numbering
-The main purpose of versioning GPflowOpt is user convenience: to keep the number of releases down, we try to combine seversal PRs into one increment. As we work towards something that we might call 1.0, including changes to the GPflowOpt API. Minor version bumps (X.1) are used for updates to follow a new GPflow or TensorFlow API, or introduce incremental new features.
+The main purpose of versioning GPflowOpt is user convenience: to keep the number of releases down, we try to combine several PRs into one increment.
 When incrementing the version number, the following tasks are required:
  - Update the version in `GPflowOpt/_version.py`
  - Add a note to `RELEASE.md`

README.md

@@ -1,12 +1,17 @@
-This is pre-release software, and as such is lacking testing, documentation and much functionality. 
-
-You can find the project proposal at: https://github.com/GPflow/GPflow/issues/397
-
-If you're interested in helping shape GPflowOpt, let us know via an issue on this repo.
-
 # GPflowOpt
-Bayesian Optimization using GPflow
+GPflowOpt is a python package for Bayesian Optimization using [GPflow](https://github.com/GPflow/GPflow), and uses [TensorFlow](http://www.tensorflow.org). It was [initiated](https://github.com/GPflow/GPflow/issues/397) and is currently maintained by [Joachim van der Herten](http://sumo.intec.ugent.be/members?q=jvanderherten) and [Ivo Couckuyt](http://sumo.intec.ugent.be/icouckuy). The full list of contributors (in alphabetical order) is Ivo Couckuyt, Tom Dhaene, James Hensman, Nicolas Knudde, Alexander G. de G. Matthews and Joachim van der Herten. Special thanks also to all [GPflow contributors](http://github.com/GPflow/GPflow/graphs/contributors) as this package would not be able to exist without their effort.
 
 [![Build Status](https://travis-ci.org/GPflow/GPflowOpt.svg?branch=master)](https://travis-ci.org/GPflow/GPflowOpt)
 [![Coverage Status](https://codecov.io/gh/GPflow/GPflowOpt/branch/master/graph/badge.svg)](https://codecov.io/gh/GPflow/GPflowOpt)
-[![Documentation Status](https://readthedocs.org/projects/gpflowopt/badge/?version=latest)](http://gpflowopt.readthedocs.io/en/latest/?badge=latest)
+[![Documentation Status](https://readthedocs.org/projects/gpflowopt/badge/?version=latest)](http://gpflowopt.readthedocs.io/en/latest/?badge=latest)
+
+# Install
+
+The easiest way to install GPflowOpt involves cloning this repository and running
+```
+pip install . --process-dependency-links
+```
+in the source directory. This also installs all required dependencies (including TensorFlow, if needed). For more detailed installation instructions, see the [documentation](https://gpflowopt.readthedocs.io/en/latest/intro.html#install).
+
+# Contributing
+If you are interested in contributing to this open source project, contact us through an issue on this repository. For more information, see the [notes for contributors](contributing.md).

RELEASE.md

@@ -1 +1,2 @@
-Pre-release
+# Release 0.1.0
+Initial release of GPflowOpt

doc/source/intro.rst

@@ -6,12 +6,11 @@ Introduction
 It makes use of TensorFlow for computation of acquisition functions, to offer scalability, and avoid implementation of gradients.
 The package was created, and is currently maintained by `Joachim van der Herten <http://sumo.intec.ugent.be/jvanderherten>`_ and `Ivo Couckuyt <http://sumo.intec.ugent.be/icouckuy>`_
 
-Currently the software is pre-release and under construction, hence it lacks a lot of functionality and testing. This documentation
-is also incomplete and under development. The project is open source: if you feel you have some relevant skills and are interested in
+The project is open source: if you feel you have some relevant skills and are interested in
 contributing then please contact us on `GitHub <https://github.com/GPflow/GPflowOpt>`_ by opening an issue or pull request.
 
 Install
---------
+-------
 1. Install package
 
 A straightforward way to install GPflowOpt is to clone its repository and run
@@ -32,9 +31,8 @@ GPflowOpt is a pure python library so you could just add it to your python path.
 The tests require some additional dependencies that need to be installed first with
 ``pip install -e .[test]``. Afterwards the tests can be run with ``python setup.py test``.
 
-Similarly, to build the documentation,
-first install the extra dependencies with ``pip install -e .[docs]``.
-Then proceed with ``python setup.py build_sphinx``.
+Similarly, to build the documentation, first install the extra dependencies with
+``pip install -e .[docs]``. Then proceed with ``python setup.py build_sphinx``.
 
 Getting started
 ---------------

gpflowopt/_version.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "pre-release"  # pragma: no cover
+__version__ = "0.1.0"  # pragma: no cover

gpflowopt/acquisition/acquisition.py

@@ -129,7 +129,8 @@ def enable_scaling(self, domain):
         Enables and configures the :class:`.DataScaler` objects wrapping the GP models.
         
         Sets the _needs_setup attribute to True so the contained models are optimized and :meth:`setup` is run again
-        right before evaluating the :class:`Acquisition` function.
+        right before evaluating the :class:`Acquisition` function. Note that the models are modified directly and
+        references to them outside of the object will also point to scaled instances.
 
         :param domain: :class:`.Domain` object, the input transform of the data scalers is configured as a transform
             from domain to the unit cube with the same dimensionality.

gpflowopt/bo.py

@@ -74,7 +74,9 @@ def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=Tr
             the points as specified by the design.
         :param bool scaling: (boolean, default true) if set to true, the outputs are normalized, and the inputs are
             scaled to a unit cube. This only affects model training: calls to acquisition.data, as well as
-            returned optima are unscaled (see :class:`~.DataScaler` for more details.)
+            returned optima are unscaled (see :class:`~.DataScaler` for more details.). Note, the models contained by
+            acquisition are modified directly, and so the references to the model outside of BayesianOptimizer now point
+            to scaled models.
         :param int hyper_draws: (optional) Enable marginalization of model hyperparameters. By default, point estimates are
             used. If this parameter set to n, n hyperparameter draws from the likelihood distribution
             are obtained using Hamiltonian MC.