automatic import of python-channelattributionopeneuler20.03

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diff --git a/python-channelattribution.spec b/python-channelattribution.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-ChannelAttribution
+Version:	2.1.3
+Release:	1
+Summary:	Markov Model for Online Multi-Channel Attribution
+License:	GPLv3
+URL:		https://channelattribution.io
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6d/00/0ff384b01cd195966f82727c3e4c087887609e9524a3d17b78e6b62b1a78/ChannelAttribution-2.1.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. ChannelAttribution implements a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data.
+
+%package -n python3-ChannelAttribution
+Summary:	Markov Model for Online Multi-Channel Attribution
+Provides:	python-ChannelAttribution
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ChannelAttribution
+Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. ChannelAttribution implements a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data.
+
+%package help
+Summary:	Development documents and examples for ChannelAttribution
+Provides:	python3-ChannelAttribution-doc
+%description help
+Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. ChannelAttribution implements a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data.
+
+%prep
+%autosetup -n ChannelAttribution-2.1.3
+
+%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-ChannelAttribution -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.3-1
+- Package Spec generated