smac 0.10.0

SMAC3, a Python implementation of 'Sequential Model-based Algorithm Configuration'.

# SMAC v3 Project

Copyright (C) 2016-2018 [AutoML Group](http://www.automl.org/)

__Attention__: This package is a re-implementation of the original SMAC tool
(see reference below).
However, the reimplementation slightly differs from the original SMAC.
For comparisons against the original SMAC, we refer to a stable release of SMAC (v2) in Java
which can be found [here](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/).

The documentation can be found [here](https://automl.github.io/SMAC3/).

Status for master branch:

[](https://travis-ci.org/automl/SMAC3)
[](https://www.codacy.com/app/automl/SMAC3?utm_source=github.com&utm_medium=referral&utm_content=automl/SMAC3&utm_campaign=Badge_Grade)
[](https://codecov.io/gh/automl/SMAC3)

Status for development branch

[](https://travis-ci.org/automl/SMAC3)
[](https://www.codacy.com/app/automl/SMAC3?utm_source=github.com&utm_medium=referral&utm_content=automl/SMAC3&utm_campaign=Badge_Grade)
[](https://codecov.io/gh/automl/SMAC3)

# OVERVIEW

SMAC is a tool for algorithm configuration to optimize the parameters of
arbitrary algorithms across a set of instances. This also includes
hyperparameter optimization of ML algorithms. The main core consists of
Bayesian Optimization in combination with a aggressive racing mechanism to
efficiently decide which of two configuration performs better.

For a detailed description of its main idea,
we refer to

Hutter, F. and Hoos, H. H. and Leyton-Brown, K.
Sequential Model-Based Optimization for General Algorithm Configuration
In: Proceedings of the conference on Learning and Intelligent OptimizatioN (LION 5)


SMAC v3 is written in Python3 and continuously tested with python3.5 and
python3.6. Its [Random Forest](https://github.com/automl/random_forest_run)
is written in C++.

# Installation

## Requirements

Besides the listed requirements (see `requirements.txt`), the random forest
used in SMAC3 requires SWIG (>= 3.0).

```apt-get install swig```


## Installation via pip

SMAC3 is available on pipy.

```pip install smac```

## Manual Installation

```
git clone https://github.com/automl/SMAC3.git && cd SMAC3
cat requirements.txt | xargs -n 1 -L 1 pip install
python setup.py install
```

## Installation in Anaconda

If you use Anaconda as your Python environment, you have to install three
packages **before** you can install SMAC:

```conda install gxx_linux-64 gcc_linux-64 swig```

# License

This program is free software: you can redistribute it and/or modify
it under the terms of the 3-clause BSD license (please see the LICENSE file).

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

You should have received a copy of the 3-clause BSD license
along with this program (see LICENSE file).
If not, see <https://opensource.org/licenses/BSD-3-Clause>.

# USAGE

The usage of SMAC v3 is mainly the same as provided with [SMAC v2.08](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/v2.08.00/manual.pdf).
It supports the same parameter configuration space syntax
(except for extended forbidden constraints) and interface to
target algorithms.

# Examples

See examples/

* examples/rosenbrock.py - example on how to optimize a Python function
* examples/spear_qcp/run.sh - example on how to optimize the SAT solver Spear
on a set of SAT formulas

# Contact

SMAC3 is developed by the [AutoML Group of the University of Freiburg](http://www.automl.org/).

If you found a bug, please report to <https://github.com/automl/SMAC3/issues>.

Our guidelines for contributing to this package can be found [here](https://github.com/automl/SMAC3/blob/master/.github/CONTRIBUTING.md)

SMAC License
============
============

BSD 3-Clause License

Copyright (c) 2016-2018, Ml4AAD Group (http://www.ml4aad.org/)
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

License of other files
======================
======================

RoBO
====

Gaussian process files are built on code from RoBO and/or are copied from RoBO: https://github.com/automl/RoBO

smac/epm/gaussian_process.py
smac/epm/gaussian_process_mcmc.py
smac/epm/gp_base_prior.py
smac/epm/gp_default_priors.py

License:

Copyright (c) 2015, automl
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

* Neither the name of RoBO nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.