Sequential Model-based Algorithm Configuration
Python Batchfile Shell
Latest commit 5775eaf Jan 22, 2018
aaronki authored and KEggensperger committed Jan 22, 2018 fixing issue #317: non-deterministic although random seed fixed (#362)
* issue #317: passing deterministic random number generator where possible, log if SMAC falls back to non-deterministic rngs and remove usage of numpy.random.RandomState.seed where ~.randint should be used

* issue #317: check deterministic behaviour in test case

* use default seed if no seed is given by user (deterministic behaviour)

* add test case for issue #369

* fix test case not working any more due to change of upper bound of random variable to MAXINT

* remove debugging code

README.md

SMAC v3 Project

Copyright (C) 2017 ML4AAD Group

Attention: This package is under heavy development and subject to change. A stable release of SMAC (v2) in Java can be found here.

The documentation can be found here.

Status for master branch:

Build Status Code Health codecov Status

Status for development branch

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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 simple 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++.

Installation

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

apt-get install swig 

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

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. It supports the same parameter configuration space syntax and interface to target algorithms. Please note that we do not support the extended parameter configuration syntax introduced in SMACv2.10.

Examples

See examples/

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

Contact

SMAC v3 is developed by the ML4AAD Group of the University of Freiburg.

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