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%global _empty_manifest_terminate_build 0
Name: python-ethnicolr
Version: 0.9.6
Release: 1
Summary: Predict Race/Ethnicity Based on Sequence of Characters in the Name
License: MIT
URL: https://github.com/appeler/ethnicolr
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f7/05/073f62a75773d4f67ab3e86079d9e0ecc0ca5200a4164c8e4a4fc1395496/ethnicolr-0.9.6.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-tensorflow
Requires: python3-tensorflow-aarch64
Requires: python3-check-manifest
Requires: python3-coverage
%description
We exploit the US census data, the Florida voting registration data, and
the Wikipedia data collected by Skiena and colleagues, to predict race
and ethnicity based on first and last name or just the last name. The granularity
at which we predict the race depends on the dataset. For instance,
Skiena et al.' Wikipedia data is at the ethnic group level, while the
census data we use in the model (the raw data has additional categories of
Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites,
Non-Hispanic Blacks, Asians, and Hispanics.
%package -n python3-ethnicolr
Summary: Predict Race/Ethnicity Based on Sequence of Characters in the Name
Provides: python-ethnicolr
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ethnicolr
We exploit the US census data, the Florida voting registration data, and
the Wikipedia data collected by Skiena and colleagues, to predict race
and ethnicity based on first and last name or just the last name. The granularity
at which we predict the race depends on the dataset. For instance,
Skiena et al.' Wikipedia data is at the ethnic group level, while the
census data we use in the model (the raw data has additional categories of
Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites,
Non-Hispanic Blacks, Asians, and Hispanics.
%package help
Summary: Development documents and examples for ethnicolr
Provides: python3-ethnicolr-doc
%description help
We exploit the US census data, the Florida voting registration data, and
the Wikipedia data collected by Skiena and colleagues, to predict race
and ethnicity based on first and last name or just the last name. The granularity
at which we predict the race depends on the dataset. For instance,
Skiena et al.' Wikipedia data is at the ethnic group level, while the
census data we use in the model (the raw data has additional categories of
Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites,
Non-Hispanic Blacks, Asians, and Hispanics.
%prep
%autosetup -n ethnicolr-0.9.6
%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-ethnicolr -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.6-1
- Package Spec generated
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