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%global _empty_manifest_terminate_build 0
Name: python-properscoring
Version: 0.1
Release: 1
Summary: Proper scoring rules in Python
License: Apache
URL: https://github.com/TheClimateCorporation/properscoring
Source0: https://mirrors.aliyun.com/pypi/web/packages/38/ac/513d2c8653ab6bc66c4502372e6e4e20ce6a136cde4c1ba9908ec36e34c1/properscoring-0.1.tar.gz
BuildArch: noarch
%description
`Proper scoring rules`_ for evaluating probabilistic forecasts in Python.
Evaluation methods that are "strictly proper" cannot be artificially improved
through hedging, which makes them fair methods for accessing the accuracy of
probabilistic forecasts. In particular, these rules are often used for
evaluating weather forecasts.
properscoring runs on both Python 2 and 3. It requires NumPy (1.8 or
later) and SciPy (any recent version should be fine). Numba is optional,
but highly encouraged: it enables significant speedups (e.g., 20x faster)
for ``crps_ensemble`` and ``threshold_brier_score``.
To install, use pip: ``pip install properscoring``.
%package -n python3-properscoring
Summary: Proper scoring rules in Python
Provides: python-properscoring
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-properscoring
`Proper scoring rules`_ for evaluating probabilistic forecasts in Python.
Evaluation methods that are "strictly proper" cannot be artificially improved
through hedging, which makes them fair methods for accessing the accuracy of
probabilistic forecasts. In particular, these rules are often used for
evaluating weather forecasts.
properscoring runs on both Python 2 and 3. It requires NumPy (1.8 or
later) and SciPy (any recent version should be fine). Numba is optional,
but highly encouraged: it enables significant speedups (e.g., 20x faster)
for ``crps_ensemble`` and ``threshold_brier_score``.
To install, use pip: ``pip install properscoring``.
%package help
Summary: Development documents and examples for properscoring
Provides: python3-properscoring-doc
%description help
`Proper scoring rules`_ for evaluating probabilistic forecasts in Python.
Evaluation methods that are "strictly proper" cannot be artificially improved
through hedging, which makes them fair methods for accessing the accuracy of
probabilistic forecasts. In particular, these rules are often used for
evaluating weather forecasts.
properscoring runs on both Python 2 and 3. It requires NumPy (1.8 or
later) and SciPy (any recent version should be fine). Numba is optional,
but highly encouraged: it enables significant speedups (e.g., 20x faster)
for ``crps_ensemble`` and ``threshold_brier_score``.
To install, use pip: ``pip install properscoring``.
%prep
%autosetup -n properscoring-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-properscoring -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1-1
- Package Spec generated
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