automatic import of python-gym-update

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+/gym_update-0.6.2.tar.gz
diff --git a/python-gym-update.spec b/python-gym-update.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-gym-update
+Version:	0.6.2
+Release:	1
+Summary:	A OpenAI Gym Env for continuous control
+License:	MIT
+URL:		https://pypi.org/project/gym-update/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/96/2e/a1b89842b773f90616ac09b0a409f5d5eb30a7e7546cf3436c1121d47d23/gym_update-0.6.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-gym
+
+%description
+# Gym-style API environment
+
+## A write up
+[Here's](https://www.overleaf.com/project/62b89d3b150bcf81e449aeb3) the most recent write up regarding the envoronment and algorithms applied to it.
+
+## Comments
+
+- in general, if we record a transition up to "Done" or if we update as soon as we reach "Done", the info collected is very little. Done is reached after 1 or 2 transitions. specify a different condition
+
+
+## Environment dynamics
+
+<img width="500" height="300" src="https://github.com/claudia-viaro/gym-update/blob/main/dynamics.png">
+
+The functions used:
+- $f_e(x^s, x^a) = \mathbb{E}[Y_e|X_e(1) = (x^s, x^a)]$: Causal mechanism determining probability of $Y_e = 1$ given $X_e(1)$. We will take $f_e(x^s, x^a) = (1 + \exp^{−x^s−x^a})^{−1}$
+- $g^a_e(\rho, x^a) \in \{g : [0, 1] \times \Omega \rightarrow \Omega \}$: Intervention process on $X^a$ in response to a predictive score $\rho$ updating $X^a_e(0) \rightarrow X^a_e(1)$
+- $\rho_e(x^s, x^a) \in \{\rho_e : \Omega^s \times \Omega^a \rightarrow [0, 1]\}$: Predictive score trained at epoch $e$
+
+
+Additional information:
+- At epoch $e$, the predictive score $\rho$ uses $X^a_e(0), X^s_e(0)$ and $Y_e$ as training data; previous epochs are ignored and $X^a_e(1), X^s_e(1)$ are not observed. The predictive score is computed at time $t=0$.
+- We allow $\rho_e$ to be an arbitrary function, but generally presume it is an estimator of $\rho_e(x^s, x^a) \approx E [Y_e|X^s_e(0) = x^s, X^a_e(0) = x^a]= f_e(x^s, g^a_e(\rho_{e-1}, x^a)) \triangleq \tilde{f}_e(x^s, x^a) $
+- $\forall e f_e = E[Y_e|X_e] = E[Y_e|X_e(1)]$: $Y_e$ depends on $X_e(1)$; that is, after any potential interventions
+- a higher value $\rho$ means a larger intervention is made (we assume $g^a_e$ to be deterministic, but random valued functions may more accurately capture the
+uncertainty linked to real-world interventions)
+
+
+
+## Naive updating
+By  ‘naive’ updating it is meant that a new score $ρ_e$ is fitted in each epoch, and then used as a drop-in replacement of an existing score $ρ_{e−1}$. It leads
+to estimates $\rho_e(x^s, x^a)$ converging as $e \rightarrow \infty$ to a setting in which $\rho_e$ accurately estimates its own effect: conceptually, $\rho_e(x^s, x^a)$ estimates the probability of $Y$ after interventions have been made on the basis of $\rho_e(x^s, x^a)$ itself. <br /> 
+
+**EPOCH 0** <br />
+**t=0** <br />
+- observe a population of patients $(X_0^a(0),X_0^s(0))_{i=1}^N$
+
+**t=1** <br />
+- there are no interventions, hence $X_0^a(1) = X_0^a(0)$
+- the risk of observing $Y = 1$ depends only on covariates at $t1$ through $f_0$ and is $E[Y_0|X_0(0) = (x^s, x^a)] =f(x^s, x^a)$
+- the score $\rho_0$ is therefore defined as $\rho_0(x^s, x^a) = f(x^s, x^a)$
+- $Y_0$ is observed 
+- analyst decides a function $\rho_0$, which is retained into epoch 1. We will use initialized actions $\theta = (\theta^0, \theta^1, \theta^2)$
+
+_The model performance under non-intervention is equivalent to performance at epoch 0_ <br />
+
+**EPOCH $>0$**
+**t=0**<br />
+- observe a new population of patients $(X_e^a(0),X_e^s(0))_{i=1}^N$
+- analyst computes $\rho_0 (X^s_e(0), Xa_e(0))$
+
+**t=1**<br />
+- $X^s_e(0)$ is not interventionable and becomes $X^s_e(1)$
+- $\rho_0$ is used to inform interventions $g^a_e$ to change values $X^a_e(1) = g_e(\rho_{e-1}(x^s, x^a), x^a)$
+- $E[Y_1]$ is determined by covariates $X^s_e(1), X^a_e(1)$
+- the score $ρ_e$ is defined as $\rho_e(x^s, x^a) = f_e(x^s, g^a_e(\rho_{e-1}(x^s, x^a), xa))  \triangleq h(\rho_{e−1} (x^s, x^a))
+- $Y_e$ is observed 
+- analyst decides a function $\rho_e$ using $X^s_e(1), X^a_e(1), Y_e$, which is retained into epoch $e+1$. We will use $\rho_e =(1 + exp^(−\theta^0 −x^s \theta^1 −x^a \beta^2 ))^{−1}$ <br />
+
+Then the episodes repeat <br />
+
+## state and action spaces:
+Action space: 3D space $\in [-2, 2]$. Actions represent the coefficients thetas of a logistic regression that will be run on the dataset of patients         <br />    
+
+Observation space: aD space $\in [0, \infty)$. States represent values for the predictive score $f_e$  <br />
+
+
+
+
+## To install
+- git clone https://github.com/claudia-viaro/gym-update.git
+- cd gym-update
+- !pip install gym-update
+- import gym
+- import gym_update
+- env =gym.make('update-v0')
+
+# To change version
+- change version to, e.g., 1.0.7 from setup.py file
+- git clone https://github.com/claudia-viaro/gym-update.git
+- cd gym-update
+- python setup.py sdist bdist_wheel
+- twine check dist/*
+- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
+
+
+
+
+%package -n python3-gym-update
+Summary:	A OpenAI Gym Env for continuous control
+Provides:	python-gym-update
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-gym-update
+# Gym-style API environment
+
+## A write up
+[Here's](https://www.overleaf.com/project/62b89d3b150bcf81e449aeb3) the most recent write up regarding the envoronment and algorithms applied to it.
+
+## Comments
+
+- in general, if we record a transition up to "Done" or if we update as soon as we reach "Done", the info collected is very little. Done is reached after 1 or 2 transitions. specify a different condition
+
+
+## Environment dynamics
+
+<img width="500" height="300" src="https://github.com/claudia-viaro/gym-update/blob/main/dynamics.png">
+
+The functions used:
+- $f_e(x^s, x^a) = \mathbb{E}[Y_e|X_e(1) = (x^s, x^a)]$: Causal mechanism determining probability of $Y_e = 1$ given $X_e(1)$. We will take $f_e(x^s, x^a) = (1 + \exp^{−x^s−x^a})^{−1}$
+- $g^a_e(\rho, x^a) \in \{g : [0, 1] \times \Omega \rightarrow \Omega \}$: Intervention process on $X^a$ in response to a predictive score $\rho$ updating $X^a_e(0) \rightarrow X^a_e(1)$
+- $\rho_e(x^s, x^a) \in \{\rho_e : \Omega^s \times \Omega^a \rightarrow [0, 1]\}$: Predictive score trained at epoch $e$
+
+
+Additional information:
+- At epoch $e$, the predictive score $\rho$ uses $X^a_e(0), X^s_e(0)$ and $Y_e$ as training data; previous epochs are ignored and $X^a_e(1), X^s_e(1)$ are not observed. The predictive score is computed at time $t=0$.
+- We allow $\rho_e$ to be an arbitrary function, but generally presume it is an estimator of $\rho_e(x^s, x^a) \approx E [Y_e|X^s_e(0) = x^s, X^a_e(0) = x^a]= f_e(x^s, g^a_e(\rho_{e-1}, x^a)) \triangleq \tilde{f}_e(x^s, x^a) $
+- $\forall e f_e = E[Y_e|X_e] = E[Y_e|X_e(1)]$: $Y_e$ depends on $X_e(1)$; that is, after any potential interventions
+- a higher value $\rho$ means a larger intervention is made (we assume $g^a_e$ to be deterministic, but random valued functions may more accurately capture the
+uncertainty linked to real-world interventions)
+
+
+
+## Naive updating
+By  ‘naive’ updating it is meant that a new score $ρ_e$ is fitted in each epoch, and then used as a drop-in replacement of an existing score $ρ_{e−1}$. It leads
+to estimates $\rho_e(x^s, x^a)$ converging as $e \rightarrow \infty$ to a setting in which $\rho_e$ accurately estimates its own effect: conceptually, $\rho_e(x^s, x^a)$ estimates the probability of $Y$ after interventions have been made on the basis of $\rho_e(x^s, x^a)$ itself. <br /> 
+
+**EPOCH 0** <br />
+**t=0** <br />
+- observe a population of patients $(X_0^a(0),X_0^s(0))_{i=1}^N$
+
+**t=1** <br />
+- there are no interventions, hence $X_0^a(1) = X_0^a(0)$
+- the risk of observing $Y = 1$ depends only on covariates at $t1$ through $f_0$ and is $E[Y_0|X_0(0) = (x^s, x^a)] =f(x^s, x^a)$
+- the score $\rho_0$ is therefore defined as $\rho_0(x^s, x^a) = f(x^s, x^a)$
+- $Y_0$ is observed 
+- analyst decides a function $\rho_0$, which is retained into epoch 1. We will use initialized actions $\theta = (\theta^0, \theta^1, \theta^2)$
+
+_The model performance under non-intervention is equivalent to performance at epoch 0_ <br />
+
+**EPOCH $>0$**
+**t=0**<br />
+- observe a new population of patients $(X_e^a(0),X_e^s(0))_{i=1}^N$
+- analyst computes $\rho_0 (X^s_e(0), Xa_e(0))$
+
+**t=1**<br />
+- $X^s_e(0)$ is not interventionable and becomes $X^s_e(1)$
+- $\rho_0$ is used to inform interventions $g^a_e$ to change values $X^a_e(1) = g_e(\rho_{e-1}(x^s, x^a), x^a)$
+- $E[Y_1]$ is determined by covariates $X^s_e(1), X^a_e(1)$
+- the score $ρ_e$ is defined as $\rho_e(x^s, x^a) = f_e(x^s, g^a_e(\rho_{e-1}(x^s, x^a), xa))  \triangleq h(\rho_{e−1} (x^s, x^a))
+- $Y_e$ is observed 
+- analyst decides a function $\rho_e$ using $X^s_e(1), X^a_e(1), Y_e$, which is retained into epoch $e+1$. We will use $\rho_e =(1 + exp^(−\theta^0 −x^s \theta^1 −x^a \beta^2 ))^{−1}$ <br />
+
+Then the episodes repeat <br />
+
+## state and action spaces:
+Action space: 3D space $\in [-2, 2]$. Actions represent the coefficients thetas of a logistic regression that will be run on the dataset of patients         <br />    
+
+Observation space: aD space $\in [0, \infty)$. States represent values for the predictive score $f_e$  <br />
+
+
+
+
+## To install
+- git clone https://github.com/claudia-viaro/gym-update.git
+- cd gym-update
+- !pip install gym-update
+- import gym
+- import gym_update
+- env =gym.make('update-v0')
+
+# To change version
+- change version to, e.g., 1.0.7 from setup.py file
+- git clone https://github.com/claudia-viaro/gym-update.git
+- cd gym-update
+- 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-update
+Provides:	python3-gym-update-doc
+%description help
+# Gym-style API environment
+
+## A write up
+[Here's](https://www.overleaf.com/project/62b89d3b150bcf81e449aeb3) the most recent write up regarding the envoronment and algorithms applied to it.
+
+## Comments
+
+- in general, if we record a transition up to "Done" or if we update as soon as we reach "Done", the info collected is very little. Done is reached after 1 or 2 transitions. specify a different condition
+
+
+## Environment dynamics
+
+<img width="500" height="300" src="https://github.com/claudia-viaro/gym-update/blob/main/dynamics.png">
+
+The functions used:
+- $f_e(x^s, x^a) = \mathbb{E}[Y_e|X_e(1) = (x^s, x^a)]$: Causal mechanism determining probability of $Y_e = 1$ given $X_e(1)$. We will take $f_e(x^s, x^a) = (1 + \exp^{−x^s−x^a})^{−1}$
+- $g^a_e(\rho, x^a) \in \{g : [0, 1] \times \Omega \rightarrow \Omega \}$: Intervention process on $X^a$ in response to a predictive score $\rho$ updating $X^a_e(0) \rightarrow X^a_e(1)$
+- $\rho_e(x^s, x^a) \in \{\rho_e : \Omega^s \times \Omega^a \rightarrow [0, 1]\}$: Predictive score trained at epoch $e$
+
+
+Additional information:
+- At epoch $e$, the predictive score $\rho$ uses $X^a_e(0), X^s_e(0)$ and $Y_e$ as training data; previous epochs are ignored and $X^a_e(1), X^s_e(1)$ are not observed. The predictive score is computed at time $t=0$.
+- We allow $\rho_e$ to be an arbitrary function, but generally presume it is an estimator of $\rho_e(x^s, x^a) \approx E [Y_e|X^s_e(0) = x^s, X^a_e(0) = x^a]= f_e(x^s, g^a_e(\rho_{e-1}, x^a)) \triangleq \tilde{f}_e(x^s, x^a) $
+- $\forall e f_e = E[Y_e|X_e] = E[Y_e|X_e(1)]$: $Y_e$ depends on $X_e(1)$; that is, after any potential interventions
+- a higher value $\rho$ means a larger intervention is made (we assume $g^a_e$ to be deterministic, but random valued functions may more accurately capture the
+uncertainty linked to real-world interventions)
+
+
+
+## Naive updating
+By  ‘naive’ updating it is meant that a new score $ρ_e$ is fitted in each epoch, and then used as a drop-in replacement of an existing score $ρ_{e−1}$. It leads
+to estimates $\rho_e(x^s, x^a)$ converging as $e \rightarrow \infty$ to a setting in which $\rho_e$ accurately estimates its own effect: conceptually, $\rho_e(x^s, x^a)$ estimates the probability of $Y$ after interventions have been made on the basis of $\rho_e(x^s, x^a)$ itself. <br /> 
+
+**EPOCH 0** <br />
+**t=0** <br />
+- observe a population of patients $(X_0^a(0),X_0^s(0))_{i=1}^N$
+
+**t=1** <br />
+- there are no interventions, hence $X_0^a(1) = X_0^a(0)$
+- the risk of observing $Y = 1$ depends only on covariates at $t1$ through $f_0$ and is $E[Y_0|X_0(0) = (x^s, x^a)] =f(x^s, x^a)$
+- the score $\rho_0$ is therefore defined as $\rho_0(x^s, x^a) = f(x^s, x^a)$
+- $Y_0$ is observed 
+- analyst decides a function $\rho_0$, which is retained into epoch 1. We will use initialized actions $\theta = (\theta^0, \theta^1, \theta^2)$
+
+_The model performance under non-intervention is equivalent to performance at epoch 0_ <br />
+
+**EPOCH $>0$**
+**t=0**<br />
+- observe a new population of patients $(X_e^a(0),X_e^s(0))_{i=1}^N$
+- analyst computes $\rho_0 (X^s_e(0), Xa_e(0))$
+
+**t=1**<br />
+- $X^s_e(0)$ is not interventionable and becomes $X^s_e(1)$
+- $\rho_0$ is used to inform interventions $g^a_e$ to change values $X^a_e(1) = g_e(\rho_{e-1}(x^s, x^a), x^a)$
+- $E[Y_1]$ is determined by covariates $X^s_e(1), X^a_e(1)$
+- the score $ρ_e$ is defined as $\rho_e(x^s, x^a) = f_e(x^s, g^a_e(\rho_{e-1}(x^s, x^a), xa))  \triangleq h(\rho_{e−1} (x^s, x^a))
+- $Y_e$ is observed 
+- analyst decides a function $\rho_e$ using $X^s_e(1), X^a_e(1), Y_e$, which is retained into epoch $e+1$. We will use $\rho_e =(1 + exp^(−\theta^0 −x^s \theta^1 −x^a \beta^2 ))^{−1}$ <br />
+
+Then the episodes repeat <br />
+
+## state and action spaces:
+Action space: 3D space $\in [-2, 2]$. Actions represent the coefficients thetas of a logistic regression that will be run on the dataset of patients         <br />    
+
+Observation space: aD space $\in [0, \infty)$. States represent values for the predictive score $f_e$  <br />
+
+
+
+
+## To install
+- git clone https://github.com/claudia-viaro/gym-update.git
+- cd gym-update
+- !pip install gym-update
+- import gym
+- import gym_update
+- env =gym.make('update-v0')
+
+# To change version
+- change version to, e.g., 1.0.7 from setup.py file
+- git clone https://github.com/claudia-viaro/gym-update.git
+- cd gym-update
+- python setup.py sdist bdist_wheel
+- twine check dist/*
+- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
+
+
+
+
+%prep
+%autosetup -n gym-update-0.6.2
+
+%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-update -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..e486756
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+148b50fb781a048b4687b04909e153d8  gym_update-0.6.2.tar.gz