%global _empty_manifest_terminate_build 0
Name:		python-ecole
Version:	0.8.1
Release:	1
Summary:	Extensible Combinatorial Optimization Learning Environments
License:	BSD-3-Clause
URL:		https://www.ecole.ai
Source0:	https://mirrors.aliyun.com/pypi/web/packages/e0/36/9595169e1dd0a8c0e14a4d2e42826f1127a23abeba33a82bc6e49c5fe9ae/ecole-0.8.1.tar.gz
BuildArch:	noarch


%description
Ecole (pronounced [ekɔl]) stands for *Extensible Combinatorial Optimization Learning
Environments* and aims to expose a number of control problems arising in combinatorial
optimization solvers as Markov
Decision Processes (*i.e.*, Reinforcement Learning environments).
Rather than trying to predict solutions to combinatorial optimization problems directly, the
philosophy behind Ecole is to work
in cooperation with a state-of-the-art Mixed Integer Linear Programming solver
that acts as a controllable algorithm.
The underlying solver used is `SCIP <https://scip.zib.de/>`_, and the user facing API is
meant to mimic the `OpenAI Gym <https://gym.openai.com/>`_ API (as much as possible).
   import ecole
   env = ecole.environment.Branching(
       reward_function=-1.5 * ecole.reward.LpIterations() ** 2,
       observation_function=ecole.observation.NodeBipartite(),
   )
   instances = ecole.instance.SetCoverGenerator()
   for _ in range(10):
       obs, action_set, reward_offset, done, info = env.reset(next(instances))
       while not done:
           obs, action_set, reward, done, info = env.step(action_set[0])

%package -n python3-ecole
Summary:	Extensible Combinatorial Optimization Learning Environments
Provides:	python-ecole
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-ecole
Ecole (pronounced [ekɔl]) stands for *Extensible Combinatorial Optimization Learning
Environments* and aims to expose a number of control problems arising in combinatorial
optimization solvers as Markov
Decision Processes (*i.e.*, Reinforcement Learning environments).
Rather than trying to predict solutions to combinatorial optimization problems directly, the
philosophy behind Ecole is to work
in cooperation with a state-of-the-art Mixed Integer Linear Programming solver
that acts as a controllable algorithm.
The underlying solver used is `SCIP <https://scip.zib.de/>`_, and the user facing API is
meant to mimic the `OpenAI Gym <https://gym.openai.com/>`_ API (as much as possible).
   import ecole
   env = ecole.environment.Branching(
       reward_function=-1.5 * ecole.reward.LpIterations() ** 2,
       observation_function=ecole.observation.NodeBipartite(),
   )
   instances = ecole.instance.SetCoverGenerator()
   for _ in range(10):
       obs, action_set, reward_offset, done, info = env.reset(next(instances))
       while not done:
           obs, action_set, reward, done, info = env.step(action_set[0])

%package help
Summary:	Development documents and examples for ecole
Provides:	python3-ecole-doc
%description help
Ecole (pronounced [ekɔl]) stands for *Extensible Combinatorial Optimization Learning
Environments* and aims to expose a number of control problems arising in combinatorial
optimization solvers as Markov
Decision Processes (*i.e.*, Reinforcement Learning environments).
Rather than trying to predict solutions to combinatorial optimization problems directly, the
philosophy behind Ecole is to work
in cooperation with a state-of-the-art Mixed Integer Linear Programming solver
that acts as a controllable algorithm.
The underlying solver used is `SCIP <https://scip.zib.de/>`_, and the user facing API is
meant to mimic the `OpenAI Gym <https://gym.openai.com/>`_ API (as much as possible).
   import ecole
   env = ecole.environment.Branching(
       reward_function=-1.5 * ecole.reward.LpIterations() ** 2,
       observation_function=ecole.observation.NodeBipartite(),
   )
   instances = ecole.instance.SetCoverGenerator()
   for _ in range(10):
       obs, action_set, reward_offset, done, info = env.reset(next(instances))
       while not done:
           obs, action_set, reward, done, info = env.step(action_set[0])

%prep
%autosetup -n ecole-0.8.1

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

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

%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.1-1
- Package Spec generated