ConfigSpace1.2.0
ConfigSpace1.2.0
Published
Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning.
pip install configspace
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Authors
Project URLs
Requires Python
>=3.8
Dependencies
- numpy
- pyparsing
- scipy
- typing_extensions
- more_itertools
- ConfigSpace
[docs,test,tooling]; extra == "dev"
- pytest
>=7; extra == "test"
- pytest-cov
; extra == "test"
- pytest_cases
; extra == "test"
- mypy
; extra == "tooling"
- pre-commit
; extra == "tooling"
- ruff
; extra == "tooling"
- types-pyyaml
; extra == "tooling"
- mkdocs
; extra == "docs"
- mkdocs-material
; extra == "docs"
- mkdocs-autorefs
; extra == "docs"
- mkdocs-gen-files
; extra == "docs"
- mkdocs-literate-nav
; extra == "docs"
- mkdocs-glightbox
; extra == "docs"
- mkdocstrings
[python]; extra == "docs"
- markdown-exec
[ansi]; extra == "docs"
- mike
; extra == "docs"
- pillow
; extra == "docs"
- cairosvg
; extra == "docs"
- black
; extra == "docs"
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/main/. 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},
}