automatic import of python-abess

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diff --git a/.gitignore b/.gitignore
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+/abess-0.4.6.tar.gz
diff --git a/python-abess.spec b/python-abess.spec
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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.nju.edu.cn/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 May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c104036
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d5438e713e1e7032004636f5f96f625e  abess-0.4.6.tar.gz