path: root/python-gym-update.spec

%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.2-1
- Package Spec generated