Copyright (C) 2016-2018 AutoML Group
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.
The documentation can be found here.
Status for master branch:
Status for development branch
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 is written in C++.
Besides the listed requirements (see requirements.txt), the random forest
used in SMAC3 requires SWIG (>= 3.0).
apt-get install swig
SMAC3 is available on pipy.
pip install smac
git clone https://github.com/automl/SMAC3.git && cd SMAC3
cat requirements.txt | xargs -n 1 -L 1 pip install
python setup.py install
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
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.
The usage of SMAC v3 is mainly the same as provided with SMAC v2.08. It supports the same parameter configuration space syntax (except for extended forbidden constraints) and interface to target algorithms.
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
SMAC3 is developed by the AutoML Group of the University of Freiburg.
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