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%global _empty_manifest_terminate_build 0
Name: python-gym-contin
Version: 1.5.0
Release: 1
Summary: A OpenAI Gym Env for continuous actions
License: MIT
URL: https://pypi.org/project/gym-contin/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/db/f9/4e01859a5d158f2bd94de2dcedecf801aa7095d8af2494179fca39fc5ee1/gym_contin-1.5.0.tar.gz
BuildArch: noarch
Requires: python3-gym
%description
# Gym-style API
The domain features a continuos state and a dicrete action space.
The environment initializes:
- cross-sectional dataset with variables X_a, X_s, Y and N observations;
- logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;
The agent:
- sees a patient (sample observation);
- predict his risk of admission \rho, using initialized parameters
- if \rho < 1/2:
- do not intervene on X_a, which stays the same
- else:
- sample an action a in [0,1]
- compute g(a, X_a) = newX_a
- intervene on X_a by updating it to newX_a
- give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
(shouldn't I fit a new logit-link? parameters are now diff?)
# To install
- git clone https://github.com/claudia-viaro/gym-contin.git
- cd gym-contin
- !pip install gym-contin
- import gym
- import gym_contin
- env =gym.make('contin-v0')
# To change version
- change version to, e.g., 1.0.7 from setup.py file
- git clone https://github.com/claudia-viaro/gym-contin.git
- cd gym-contin
- python setup.py sdist bdist_wheel
- twine check dist/*
- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
%package -n python3-gym-contin
Summary: A OpenAI Gym Env for continuous actions
Provides: python-gym-contin
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-gym-contin
# Gym-style API
The domain features a continuos state and a dicrete action space.
The environment initializes:
- cross-sectional dataset with variables X_a, X_s, Y and N observations;
- logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;
The agent:
- sees a patient (sample observation);
- predict his risk of admission \rho, using initialized parameters
- if \rho < 1/2:
- do not intervene on X_a, which stays the same
- else:
- sample an action a in [0,1]
- compute g(a, X_a) = newX_a
- intervene on X_a by updating it to newX_a
- give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
(shouldn't I fit a new logit-link? parameters are now diff?)
# To install
- git clone https://github.com/claudia-viaro/gym-contin.git
- cd gym-contin
- !pip install gym-contin
- import gym
- import gym_contin
- env =gym.make('contin-v0')
# To change version
- change version to, e.g., 1.0.7 from setup.py file
- git clone https://github.com/claudia-viaro/gym-contin.git
- cd gym-contin
- python setup.py sdist bdist_wheel
- twine check dist/*
- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
%package help
Summary: Development documents and examples for gym-contin
Provides: python3-gym-contin-doc
%description help
# Gym-style API
The domain features a continuos state and a dicrete action space.
The environment initializes:
- cross-sectional dataset with variables X_a, X_s, Y and N observations;
- logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;
The agent:
- sees a patient (sample observation);
- predict his risk of admission \rho, using initialized parameters
- if \rho < 1/2:
- do not intervene on X_a, which stays the same
- else:
- sample an action a in [0,1]
- compute g(a, X_a) = newX_a
- intervene on X_a by updating it to newX_a
- give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
(shouldn't I fit a new logit-link? parameters are now diff?)
# To install
- git clone https://github.com/claudia-viaro/gym-contin.git
- cd gym-contin
- !pip install gym-contin
- import gym
- import gym_contin
- env =gym.make('contin-v0')
# To change version
- change version to, e.g., 1.0.7 from setup.py file
- git clone https://github.com/claudia-viaro/gym-contin.git
- cd gym-contin
- python setup.py sdist bdist_wheel
- twine check dist/*
- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
%prep
%autosetup -n gym-contin-1.5.0
%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-gym-contin -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.5.0-1
- Package Spec generated
|
