%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