22df09315f
* r-igraph: find old packages in the archive Fixes #6104. * r-irlba: find old packages in the archive Fixes #6101. * r-lazyeval: find old packages in the archive Fixes #6102. * packages: set list_url for all packages hosted on CRAN * packages: consistently use the CRAN archive URLs
51 lines
2.6 KiB
Python
51 lines
2.6 KiB
Python
##############################################################################
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# Copyright (c) 2013-2017, 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 RRocr(RPackage):
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"""ROC graphs, sensitivity/specificity curves, lift charts,
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and precision/recall plots are popular examples of trade-off
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visualizations for specific pairs of performance measures. ROCR
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is a flexible tool for creating cutoff-parameterized 2D performance
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curves by freely combining two from over 25 performance measures
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(new performance measures can be added using a standard interface).
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Curves from different cross-validation or bootstrapping runs can
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be averaged by different methods, and standard deviations, standard
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errors or box plots can be used to visualize the variability across
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the runs. The parameterization can be visualized by printing cutoff
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values at the corresponding curve positions, or by coloring the
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curve according to cutoff. All components of a performance plot
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can be quickly adjusted using a flexible parameter dispatching
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mechanism. Despite its flexibility, ROCR is easy to use, with only
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three commands and reasonable default values for all optional
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parameters."""
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homepage = "https://cran.r-project.org/package=ROCR"
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url = "https://cran.rstudio.com/src/contrib/ROCR_1.0-7.tar.gz"
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list_url = "https://cran.r-project.org/src/contrib/Archive/ROCR"
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version('1.0-7', '46cbd43ae87fc4e1eff2109529a4820e')
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depends_on('r-gplots', type=('build', 'run'))
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