94afb2430c
* Add r-bbmisc
* Add r-dicekriging
* Add r-lhs
* Add r-mco
* Add r-misc3d
* Add r-mlr
* Remove boilerplate from r-mlr package
* Add r-mlrMBO
* Add r-parallelmap
* Add r-paramhelpers
* Add r-plot3d
* Add r-rgenoud
* Add r-smoof
* Add r-rinside 0.2.14
* Fix flake8 issues
* Add specific required versions
* Add more up-to-date versions of r-{mco, mlr, mlrMBO, smoof}
59 lines
3.0 KiB
Python
59 lines
3.0 KiB
Python
##############################################################################
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# Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC.
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# Produced at the Lawrence Livermore National Laboratory.
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#
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# This file is part of Spack.
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# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
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# LLNL-CODE-647188
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#
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# For details, see https://github.com/spack/spack
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# Please also see the NOTICE and LICENSE files for our notice and the LGPL.
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License (as
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# published by the Free Software Foundation) version 2.1, February 1999.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and
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# conditions of the GNU Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this program; if not, write to the Free Software
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# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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##############################################################################
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from spack import *
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class RMlrmbo(RPackage):
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"""Flexible and comprehensive R toolbox for model-based optimization
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('MBO'), also known as Bayesian optimization. It is designed for both
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single- and multi-objective optimization with mixed continuous,
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categorical and conditional parameters. The machine learning toolbox
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'mlr' provide dozens of regression learners to model the performance of
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the target algorithm with respect to the parameter settings. It provides
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many different infill criteria to guide the search process. Additional
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features include multi-point batch proposal, parallel execution as well
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as visualization and sophisticated logging mechanisms, which is
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especially useful for teaching and understanding of algorithm behavior.
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'mlrMBO' is implemented in a modular fashion, such that single
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components can be easily replaced or adapted by the user for specific
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use cases."""
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homepage = "https://github.com/mlr-org/mlrMBO/"
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url = "https://cran.r-project.org/src/contrib/mlrMBO_1.1.1.tar.gz"
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list_url = "https://cran.r-project.org/src/contrib/Archive/mlrMBO"
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version('1.1.1', '9a35b41ceb8754111af294dee0ae76e0')
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version('1.1.0', '9e27ff8498225d24863b8da758d2918e')
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depends_on('r-mlr@2.10:', type=('build', 'run'))
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depends_on('r-paramhelpers@1.10:', type=('build', 'run'))
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depends_on('r-smoof@1.5.1:', type=('build', 'run'))
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depends_on('r-backports@1.1.0:', type=('build', 'run'))
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depends_on('r-bbmisc@1.11:', type=('build', 'run'))
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depends_on('r-checkmate@1.8.2:', type=('build', 'run'))
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depends_on('r-data-table', type=('build', 'run'))
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depends_on('r-lhs', type=('build', 'run'))
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depends_on('r-parallelmap@1.3:', type=('build', 'run'))
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