Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning.
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Author: Matthias Feurer, Katharina Eggensperger, Syed Mohsin Ali, Christina Hernandez Wunsch, Julien-Charles Levesque, Jost Tobias Springenberg, Philipp Mueller, Marius Lindauer, Jorn Tuyls, Eddie Bergman, Arjun Krishnakumar
Maintainer: Matthias Feurer, Eddie Bergman, Arjun Krishnakumar
Requires Python: >=3.8
Classifiers
Programming Language
- Python :: 3.8
- Python :: 3.9
- Python :: 3.10
- Python :: 3.11
- Python :: 3.12
Natural Language
- English
Intended Audience
- Developers
- Education
- Science/Research
License
- OSI Approved :: BSD License
Operating System
- POSIX :: Linux
- Microsoft :: Windows
- MacOS
Topic
- Scientific/Engineering :: Artificial Intelligence
- Scientific/Engineering
- Software Development
ConfigSpace
A simple Python module implementing a domain specific language to manage
configuration spaces for algorithm configuration and hyperparameter optimization tasks.
Distributed under BSD 3-clause, see LICENSE except all files in the directory
ConfigSpace.nx, which are copied from the networkx package and licensed
under a BSD license.
The documentation can be found at https://automl.github.io/ConfigSpace/latest/. Further examples can be found in the SMAC documentation.
Minimum Example
from ConfigSpace import ConfigurationSpace
cs = ConfigurationSpace(
name="myspace",
space={
"a": (0.1, 1.5), # UniformFloat
"b": (2, 10), # UniformInt
"c": ["mouse", "cat", "dog"], # Categorical
},
)
configs = cs.sample_configuration(2)
Citing the ConfigSpace
@article{
title = {BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters},
author = {M. Lindauer and K. Eggensperger and M. Feurer and A. Biedenkapp and J. Marben and P. Müller and F. Hutter},
journal = {arXiv:1908.06756 {[cs.LG]}},
date = {2019},
}
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