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@@ -0,0 +1 @@ +/baycomp-1.0.2.tar.gz diff --git a/python-baycomp.spec b/python-baycomp.spec new file mode 100644 index 0000000..e82b207 --- /dev/null +++ b/python-baycomp.spec @@ -0,0 +1,186 @@ +%global _empty_manifest_terminate_build 0 +Name: python-baycomp +Version: 1.0.2 +Release: 1 +Summary: Bayesian tests for comparison of classifiers +License: MIT License +URL: https://github.com/janezd/baycomp.git +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6e/f7/49f0cb5f1d1b2421036360d59da131c69224cfcfb483def506831b6515d4/baycomp-1.0.2.tar.gz +BuildArch: noarch + +Requires: python3-matplotlib +Requires: python3-numpy +Requires: python3-scipy + +%description +Baycomp is a library for Bayesian comparison of classifiers. +Functions compare two classifiers on one or on multiple data sets. They +compute three probabilities: the probability that the first classifier has +higher scores than the second, the probability that differences are within +the region of practical equivalence (rope), or that the second classifier +has higher scores. We will refer to this probabilities as `p_left`, `p_rope` +and `p_right`. If the argument `rope` is omitted (or set to zero), functions +return only `p_left` and `p_right`. +The region of practical equivalence (rope) is specified by the caller and +should correspond to what is "equivalent" in practice; for instance, +classification accuracies that differ by less than 0.5 may be called +equivalent. +Similarly, whether higher scores are better or worse depends upon the type +of the score. +The library can also plot the posterior distributions. +The library can be used in three ways. +1. Two shortcut functions can be used for comparison on single + and on multiple data sets. If `nbc` and `j48` contain a list of average + classification accuracies of naive Bayesian classifier and J48 on a + collection of data sets, we can call + >>> two_on_multiple(nbc, j48, rope=1) + (0.23124, 0.00666, 0.7621) + (Actual results may differ due to Monte Carlo sampling.) + With some additional arguments, the function can also plot the posterior + distribution from which these probabilities came. +2. Tests are packed into test classes. The above call is equivalent to + >>> SignedRankTest.probs(nbc, j48, rope=1) + (0.23124, 0.00666, 0.7621) + and to get a plot, we call + >>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48")) + To switch to another test, use another class:: + >>> SignTest.probs(nbc, j48, rope=1) + (0.26508, 0.13274, 0.60218) +3. Finally, we can construct and query sampled posterior distributions. + >>> posterior = SignedRankTest(nbc, j48, rope=0.5) + >>> posterior.probs() + (0.23124, 0.00666, 0.7621) + >>> posterior.plot(names=("nbc", "j48")) + +%package -n python3-baycomp +Summary: Bayesian tests for comparison of classifiers +Provides: python-baycomp +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-baycomp +Baycomp is a library for Bayesian comparison of classifiers. +Functions compare two classifiers on one or on multiple data sets. They +compute three probabilities: the probability that the first classifier has +higher scores than the second, the probability that differences are within +the region of practical equivalence (rope), or that the second classifier +has higher scores. We will refer to this probabilities as `p_left`, `p_rope` +and `p_right`. If the argument `rope` is omitted (or set to zero), functions +return only `p_left` and `p_right`. +The region of practical equivalence (rope) is specified by the caller and +should correspond to what is "equivalent" in practice; for instance, +classification accuracies that differ by less than 0.5 may be called +equivalent. +Similarly, whether higher scores are better or worse depends upon the type +of the score. +The library can also plot the posterior distributions. +The library can be used in three ways. +1. Two shortcut functions can be used for comparison on single + and on multiple data sets. If `nbc` and `j48` contain a list of average + classification accuracies of naive Bayesian classifier and J48 on a + collection of data sets, we can call + >>> two_on_multiple(nbc, j48, rope=1) + (0.23124, 0.00666, 0.7621) + (Actual results may differ due to Monte Carlo sampling.) + With some additional arguments, the function can also plot the posterior + distribution from which these probabilities came. +2. Tests are packed into test classes. The above call is equivalent to + >>> SignedRankTest.probs(nbc, j48, rope=1) + (0.23124, 0.00666, 0.7621) + and to get a plot, we call + >>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48")) + To switch to another test, use another class:: + >>> SignTest.probs(nbc, j48, rope=1) + (0.26508, 0.13274, 0.60218) +3. Finally, we can construct and query sampled posterior distributions. + >>> posterior = SignedRankTest(nbc, j48, rope=0.5) + >>> posterior.probs() + (0.23124, 0.00666, 0.7621) + >>> posterior.plot(names=("nbc", "j48")) + +%package help +Summary: Development documents and examples for baycomp +Provides: python3-baycomp-doc +%description help +Baycomp is a library for Bayesian comparison of classifiers. +Functions compare two classifiers on one or on multiple data sets. They +compute three probabilities: the probability that the first classifier has +higher scores than the second, the probability that differences are within +the region of practical equivalence (rope), or that the second classifier +has higher scores. We will refer to this probabilities as `p_left`, `p_rope` +and `p_right`. If the argument `rope` is omitted (or set to zero), functions +return only `p_left` and `p_right`. +The region of practical equivalence (rope) is specified by the caller and +should correspond to what is "equivalent" in practice; for instance, +classification accuracies that differ by less than 0.5 may be called +equivalent. +Similarly, whether higher scores are better or worse depends upon the type +of the score. +The library can also plot the posterior distributions. +The library can be used in three ways. +1. Two shortcut functions can be used for comparison on single + and on multiple data sets. If `nbc` and `j48` contain a list of average + classification accuracies of naive Bayesian classifier and J48 on a + collection of data sets, we can call + >>> two_on_multiple(nbc, j48, rope=1) + (0.23124, 0.00666, 0.7621) + (Actual results may differ due to Monte Carlo sampling.) + With some additional arguments, the function can also plot the posterior + distribution from which these probabilities came. +2. Tests are packed into test classes. The above call is equivalent to + >>> SignedRankTest.probs(nbc, j48, rope=1) + (0.23124, 0.00666, 0.7621) + and to get a plot, we call + >>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48")) + To switch to another test, use another class:: + >>> SignTest.probs(nbc, j48, rope=1) + (0.26508, 0.13274, 0.60218) +3. Finally, we can construct and query sampled posterior distributions. + >>> posterior = SignedRankTest(nbc, j48, rope=0.5) + >>> posterior.probs() + (0.23124, 0.00666, 0.7621) + >>> posterior.plot(names=("nbc", "j48")) + +%prep +%autosetup -n baycomp-1.0.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-baycomp -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.2-1 +- Package Spec generated @@ -0,0 +1 @@ +b7a16e379b748427ef321be7e19032a4 baycomp-1.0.2.tar.gz |