{ "info": { "author": "Marius Lindauer, Matthias Feurer, Katharina Eggensperger, Joshua Marben, Andr\u00e9 Biedenkapp, Aaron Klein, Stefan Falkner and Frank Hutter", "author_email": "\"fh@cs.uni-freiburg.de\"", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: BSD License", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Utilities" ], "description": "# SMAC v3 Project\n\nCopyright (C) 2016-2018 [AutoML Group](http://www.automl.org/)\n\n__Attention__: This package is a re-implementation of the original SMAC tool\n(see reference below).\nHowever, the reimplementation slightly differs from the original SMAC.\nFor comparisons against the original SMAC, we refer to a stable release of SMAC (v2) in Java\nwhich can be found [here](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/).\n\nThe documentation can be found [here](https://automl.github.io/SMAC3/).\n\nStatus for master branch:\n\n[![Build Status](https://travis-ci.org/automl/SMAC3.svg?branch=master)](https://travis-ci.org/automl/SMAC3)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/58f47a4bd25e45c9a4901ebca68118ff?branch=master)](https://www.codacy.com/app/automl/SMAC3?utm_source=github.com&utm_medium=referral&utm_content=automl/SMAC3&utm_campaign=Badge_Grade)\n[![codecov Status](https://codecov.io/gh/automl/SMAC3/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)\n\nStatus for development branch\n\n[![Build Status](https://travis-ci.org/automl/SMAC3.svg?branch=development)](https://travis-ci.org/automl/SMAC3)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/58f47a4bd25e45c9a4901ebca68118ff?branch=development)](https://www.codacy.com/app/automl/SMAC3?utm_source=github.com&utm_medium=referral&utm_content=automl/SMAC3&utm_campaign=Badge_Grade)\n[![codecov](https://codecov.io/gh/automl/SMAC3/branch/development/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)\n\n# OVERVIEW\n\nSMAC is a tool for algorithm configuration to optimize the parameters of\narbitrary algorithms across a set of instances. This also includes\nhyperparameter optimization of ML algorithms. The main core consists of\nBayesian Optimization in combination with a aggressive racing mechanism to\nefficiently decide which of two configuration performs better.\n\nFor a detailed description of its main idea,\nwe refer to\n\n Hutter, F. and Hoos, H. H. and Leyton-Brown, K.\n Sequential Model-Based Optimization for General Algorithm Configuration\n In: Proceedings of the conference on Learning and Intelligent OptimizatioN (LION 5)\n\n\nSMAC v3 is written in Python3 and continuously tested with python3.5 and\npython3.6. Its [Random Forest](https://github.com/automl/random_forest_run)\nis written in C++.\n\n# Installation\n\n## Requirements\n\nBesides the listed requirements (see `requirements.txt`), the random forest\nused in SMAC3 requires SWIG (>= 3.0).\n\n```apt-get install swig```\n\n\n## Installation via pip\n\nSMAC3 is available on PyPI.\n\n```pip install smac```\n\n## Manual Installation\n\n```\ngit clone https://github.com/automl/SMAC3.git && cd SMAC3\ncat requirements.txt | xargs -n 1 -L 1 pip install\npip install .\n```\n\n## Installation in Anaconda\n\nIf you use Anaconda as your Python environment, you have to install three\npackages **before** you can install SMAC:\n\n```conda install gxx_linux-64 gcc_linux-64 swig```\n\n## Optional dependencies\n\nSMAC3 comes with a set of optional dependencies that can be installed using\n[setuptools extras](https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras-optional-features-with-their-own-dependencies):\n\n- `lhd`: Latin hypercube design\n- `gp`: Gaussian process models\n\nThese can be installed from PyPI or manually:\n\n```\n# from PyPI\npip install smac[gp]\n\n# manually\npip install .[gp,lhd]\n```\n\nFor convenience there is also an `all` meta-dependency that installs all optional dependencies:\n```\npip install smac[all]\n```\n\n# License\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the 3-clause BSD license (please see the LICENSE file).\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\nYou should have received a copy of the 3-clause BSD license\nalong with this program (see LICENSE file).\nIf not, see .\n\n# USAGE\n\nThe usage of SMAC v3 is mainly the same as provided with [SMAC v2.08](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/v2.08.00/manual.pdf).\nIt supports the same parameter configuration space syntax\n(except for extended forbidden constraints) and interface to\ntarget algorithms.\n\n# Examples\n\nSee examples/\n\n * examples/rosenbrock.py - example on how to optimize a Python function\n * examples/spear_qcp/run.sh - example on how to optimize the SAT solver Spear\n on a set of SAT formulas\n\n# Contact\n\nSMAC3 is developed by the [AutoML Group of the University of Freiburg](http://www.automl.org/).\n\nIf you found a bug, please report to .\n\nOur guidelines for contributing to this package can be found [here](https://github.com/automl/SMAC3/blob/master/.github/CONTRIBUTING.md)\n\nSMAC License\n============\n============\n\nBSD 3-Clause License\n\nCopyright (c) 2016-2018, Ml4AAD Group (http://www.ml4aad.org/)\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nLicense of other files\n======================\n======================\n\nRoBO\n====\n\nGaussian process files are built on code from RoBO and/or are copied from RoBO: https://github.com/automl/RoBO\n\nsmac/epm/gaussian_process.py\nsmac/epm/gaussian_process_mcmc.py\nsmac/epm/gp_base_prior.py\nsmac/epm/gp_default_priors.py\n\nLicense:\n\nCopyright (c) 2015, automl\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of RoBO nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "", "keywords": "machine learning,algorithm configuration,hyperparameter optimization,tuning", "license": "3-clause BSD", "maintainer": "", "maintainer_email": "", "name": "smac", "package_url": "https://pypi.org/project/smac/", "platform": "Linux", "project_url": "https://pypi.org/project/smac/", 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