32 lines
1.6 KiB
Python
32 lines
1.6 KiB
Python
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
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# Spack Project Developers. See the top-level COPYRIGHT file for details.
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#
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# SPDX-License-Identifier: (Apache-2.0 OR MIT)
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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://cloud.r-project.org/package=ROCR"
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url = "https://cloud.r-project.org/src/contrib/ROCR_1.0-7.tar.gz"
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list_url = "https://cloud.r-project.org/src/contrib/Archive/ROCR"
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version('1.0-7', sha256='e7ef710f847e441a48b20fdc781dbc1377f5a060a5ee635234053f7a2a435ec9')
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depends_on('r-gplots', type=('build', 'run'))
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