ConfigSpace

Name	ConfigSpace JSON
Version	1.2.1 JSON
	download
home_page	None
Summary	Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning.
upload_time	2024-11-21 10:18:39
maintainer	None
docs_url	None
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
requires_python	>=3.8
license	# ConfigSpace Copyright (c) 2014-2021, ConfigSpace developers Matthias Feurer Katharina Eggensperger and others (see commit history). 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 <organization> 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 <COPYRIGHT HOLDER> 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. # networkx (nx subpackage) Copyright (C) 2004-2014, NetworkX Developers Aric Hagberg <hagberg@lanl.gov> Dan Schult <dschult@colgate.edu> Pieter Swart <swart@lanl.gov> 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 NetworkX Developers 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 OWNER 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.
keywords	algorithm configuration hyperparameter optimization empirical evaluation black box
VCS
bugtrack_url
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            # 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/](https://automl.github.io/ConfigSpace/latest/).
Further examples can be found in the [SMAC documentation](https://automl.github.io/SMAC3/main/examples/index.html).

## Minimum Example

```python
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

```bibtex
@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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    "description": "# ConfigSpace\n\nA simple Python module implementing a domain specific language to manage\nconfiguration spaces for algorithm configuration and hyperparameter optimization tasks.  \nDistributed under BSD 3-clause, see LICENSE except all files in the directory\nConfigSpace.nx, which are copied from the networkx package and licensed\nunder a BSD license.\n\nThe documentation can be found\nat [https://automl.github.io/ConfigSpace/latest/](https://automl.github.io/ConfigSpace/latest/).\nFurther examples can be found in the [SMAC documentation](https://automl.github.io/SMAC3/main/examples/index.html).\n\n## Minimum Example\n\n```python\nfrom ConfigSpace import ConfigurationSpace\n\ncs = ConfigurationSpace(\n    name=\"myspace\",\n    space={\n        \"a\": (0.1, 1.5),  # UniformFloat\n        \"b\": (2, 10),  # UniformInt\n        \"c\": [\"mouse\", \"cat\", \"dog\"],  # Categorical\n    },\n)\n\nconfigs = cs.sample_configuration(2)\n```\n\n## Citing the ConfigSpace\n\n```bibtex\n@article{\n    title   = {BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters},\n    author  = {M. Lindauer and K. Eggensperger and M. Feurer and A. Biedenkapp and J. Marben and P. M\u00fcller and F. Hutter},\n    journal = {arXiv:1908.06756 {[cs.LG]}},\n    date    = {2019},\n}\n```\n",
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