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%global _empty_manifest_terminate_build 0
Name: python-abess
Version: 0.4.6
Release: 1
Summary: abess: Fast Best Subset Selection
License: GPL-3
URL: https://abess.readthedocs.io
Source0: https://mirrors.aliyun.com/pypi/web/packages/1f/27/0abee072f9378ea41d484b0fcc3e67b5ff10380539da7321b234adbc182d/abess-0.4.6.tar.gz
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-scipy
Requires: python3-scikit-learn
%description
**abess** (Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e.,
find a small subset of predictors such that the resulting model is expected to have the highest accuracy.
The selection for best subset shows great value in scientific researches and practical application.
For example, clinicians wants to know whether a patient is health or not
based on the expression level of a few of important genes.
This library implements a generic algorithm framework to find the optimal solution in an extremely fast way [#1abess]_.
This framework now supports the detection of best subset under:
`linear regression`_, `(multi-class) classification`_, `censored-response modeling`_ [#4sksurv]_,
`multi-response modeling (a.k.a. multi-tasks learning)`_, etc.
It also supports the variants of best subset selection like
`group best subset selection`_ [#2gbes]_ and `nuisance best subset selection`_ [#3nbes]_.
Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial [#1abess]_ [#2gbes]_.
%package -n python3-abess
Summary: abess: Fast Best Subset Selection
Provides: python-abess
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-abess
**abess** (Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e.,
find a small subset of predictors such that the resulting model is expected to have the highest accuracy.
The selection for best subset shows great value in scientific researches and practical application.
For example, clinicians wants to know whether a patient is health or not
based on the expression level of a few of important genes.
This library implements a generic algorithm framework to find the optimal solution in an extremely fast way [#1abess]_.
This framework now supports the detection of best subset under:
`linear regression`_, `(multi-class) classification`_, `censored-response modeling`_ [#4sksurv]_,
`multi-response modeling (a.k.a. multi-tasks learning)`_, etc.
It also supports the variants of best subset selection like
`group best subset selection`_ [#2gbes]_ and `nuisance best subset selection`_ [#3nbes]_.
Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial [#1abess]_ [#2gbes]_.
%package help
Summary: Development documents and examples for abess
Provides: python3-abess-doc
%description help
**abess** (Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e.,
find a small subset of predictors such that the resulting model is expected to have the highest accuracy.
The selection for best subset shows great value in scientific researches and practical application.
For example, clinicians wants to know whether a patient is health or not
based on the expression level of a few of important genes.
This library implements a generic algorithm framework to find the optimal solution in an extremely fast way [#1abess]_.
This framework now supports the detection of best subset under:
`linear regression`_, `(multi-class) classification`_, `censored-response modeling`_ [#4sksurv]_,
`multi-response modeling (a.k.a. multi-tasks learning)`_, etc.
It also supports the variants of best subset selection like
`group best subset selection`_ [#2gbes]_ and `nuisance best subset selection`_ [#3nbes]_.
Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial [#1abess]_ [#2gbes]_.
%prep
%autosetup -n abess-0.4.6
%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-abess -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.6-1
- Package Spec generated
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