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diff --git a/python-shparkley.spec b/python-shparkley.spec new file mode 100644 index 0000000..99a0a88 --- /dev/null +++ b/python-shparkley.spec @@ -0,0 +1,88 @@ +%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 |