34 lines
1.6 KiB
Python
34 lines
1.6 KiB
Python
# Copyright 2013-2024 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.package import *
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class RRocr(RPackage):
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"""Visualizing the Performance of Scoring Classifiers.
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ROC graphs, sensitivity/specificity curves, lift charts, and
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precision/recall plots are popular examples of trade-off visualizations for
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specific pairs of performance measures. ROCR is a flexible tool for
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creating cutoff-parameterized 2D performance curves by freely combining two
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from over 25 performance measures (new performance measures can be added
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using a standard interface). Curves from different cross-validation or
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bootstrapping runs can be averaged by different methods, and standard
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deviations, standard errors or box plots can be used to visualize the
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variability across the runs. The parameterization can be visualized by
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printing cutoff values at the corresponding curve positions, or by coloring
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the curve according to cutoff. All components of a performance plot can be
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quickly adjusted using a flexible parameter dispatching mechanism. Despite
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its flexibility, ROCR is easy to use, with only three commands and
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reasonable default values for all optional parameters."""
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cran = "ROCR"
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version("1.0-11", sha256="57385a773220a3aaef5b221a68b2d9c2a94794d4f9e9fc3c1eb9521767debb2a")
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version("1.0-7", sha256="e7ef710f847e441a48b20fdc781dbc1377f5a060a5ee635234053f7a2a435ec9")
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depends_on("r@3.6:", type=("build", "run"), when="@1.0-11:")
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depends_on("r-gplots", type=("build", "run"))
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