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%global _empty_manifest_terminate_build 0
Name: python-skater
Version: 1.1.2
Release: 1
Summary: Model Interpretation Library
License: MIT
URL: https://github.com/datascienceinc/skater/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5a/99/aa0b52e709a621dfae9fbf8359c9f1ee6d2272e7f53cd2815284e088ec74/skater-1.1.2.tar.gz
BuildArch: noarch
%description
Skater is a python package for interpreting(via post-hoc evaluation/rule extraction) predictive models.
With Skater, you can unpack the internal mechanics of arbitrary models; as long
as you can obtain inputs, and use a function to obtain outputs, you can use
Skater to learn about the models internal decision policies.
The package was originally developed by Aaron Kramer, Pramit Choudhary and internal DataScience Team at DataScience.com
to help enable practitioners explain and interpret predictive "black boxes" preferably in a human interpretable way.
%package -n python3-skater
Summary: Model Interpretation Library
Provides: python-skater
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-skater
Skater is a python package for interpreting(via post-hoc evaluation/rule extraction) predictive models.
With Skater, you can unpack the internal mechanics of arbitrary models; as long
as you can obtain inputs, and use a function to obtain outputs, you can use
Skater to learn about the models internal decision policies.
The package was originally developed by Aaron Kramer, Pramit Choudhary and internal DataScience Team at DataScience.com
to help enable practitioners explain and interpret predictive "black boxes" preferably in a human interpretable way.
%package help
Summary: Development documents and examples for skater
Provides: python3-skater-doc
%description help
Skater is a python package for interpreting(via post-hoc evaluation/rule extraction) predictive models.
With Skater, you can unpack the internal mechanics of arbitrary models; as long
as you can obtain inputs, and use a function to obtain outputs, you can use
Skater to learn about the models internal decision policies.
The package was originally developed by Aaron Kramer, Pramit Choudhary and internal DataScience Team at DataScience.com
to help enable practitioners explain and interpret predictive "black boxes" preferably in a human interpretable way.
%prep
%autosetup -n skater-1.1.2
%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-skater -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1
- Package Spec generated
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