%global _empty_manifest_terminate_build 0
Name:		python-shparkley
Version:	1.0.1
Release:	1
Summary:	Scaling Shapley Value computation using Spark
License:	BSD License
URL:		https://github.com/Affirm/shparkley
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/45/d3/cc2bdceda131aee61f15e9e734d4ed99c1132e9cb5e9f9f70913174d98f1/shparkley-1.0.1.tar.gz
BuildArch:	noarch

Requires:	python3-future
Requires:	python3-mock
Requires:	python3-numpy
Requires:	python3-pyspark

%description
Shparkley is a PySpark implementation of
`Shapley values <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf>`_
which uses a `monte-carlo approximation <https://link.springer.com/article/10.1007/s10115-013-0679-x>`_ algorithm.
Given a dataset and machine learning model, Shparkley can compute Shapley values for all features for a feature vector.
Shparkley also handles training weights and is model-agnostic.

%package -n python3-shparkley
Summary:	Scaling Shapley Value computation using Spark
Provides:	python-shparkley
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-shparkley
Shparkley is a PySpark implementation of
`Shapley values <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf>`_
which uses a `monte-carlo approximation <https://link.springer.com/article/10.1007/s10115-013-0679-x>`_ algorithm.
Given a dataset and machine learning model, Shparkley can compute Shapley values for all features for a feature vector.
Shparkley also handles training weights and is model-agnostic.

%package help
Summary:	Development documents and examples for shparkley
Provides:	python3-shparkley-doc
%description help
Shparkley is a PySpark implementation of
`Shapley values <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf>`_
which uses a `monte-carlo approximation <https://link.springer.com/article/10.1007/s10115-013-0679-x>`_ algorithm.
Given a dataset and machine learning model, Shparkley can compute Shapley values for all features for a feature vector.
Shparkley also handles training weights and is model-agnostic.

%prep
%autosetup -n shparkley-1.0.1

%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-shparkley -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.1-1
- Package Spec generated