automatic import of python-ecoleopeneuler20.03

3 files changed, 131 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..8f68ede 100644
--- a/.gitignore
+++ b/.gitignore
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+/ecole-0.8.1.tar.gz
diff --git a/python-ecole.spec b/python-ecole.spec
new file mode 100644
index 0000000..462be85
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+++ b/python-ecole.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..95cf3a1
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+0853c1e994c3a7668a7883f29a6bec83  ecole-0.8.1.tar.gz