%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 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.
**EPOCH 0**
**t=0**
- observe a population of patients $(X_0^a(0),X_0^s(0))_{i=1}^N$ **t=1**
- 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_
**EPOCH $>0$** **t=0**
- 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**
- $X^s_e(0)$ 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 $\rho_e =(1 + exp^(−\theta^0 −x^s \theta^1 −x^a \beta^2 ))^{−1}$
Then the episodes repeat
## 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
Observation space: aD space $\in [0, \infty)$. States represent values for the predictive score $f_e$
## 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 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.
**EPOCH 0**
**t=0**
- observe a population of patients $(X_0^a(0),X_0^s(0))_{i=1}^N$ **t=1**
- 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_
**EPOCH $>0$** **t=0**
- 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**
- $X^s_e(0)$ 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 $\rho_e =(1 + exp^(−\theta^0 −x^s \theta^1 −x^a \beta^2 ))^{−1}$
Then the episodes repeat
## 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
Observation space: aD space $\in [0, \infty)$. States represent values for the predictive score $f_e$
## 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 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.
**EPOCH 0**
**t=0**
- observe a population of patients $(X_0^a(0),X_0^s(0))_{i=1}^N$ **t=1**
- 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_
**EPOCH $>0$** **t=0**
- 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**
- $X^s_e(0)$ 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 $\rho_e =(1 + exp^(−\theta^0 −x^s \theta^1 −x^a \beta^2 ))^{−1}$
Then the episodes repeat
## 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
Observation space: aD space $\in [0, \infty)$. States represent values for the predictive score $f_e$
## 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 - 0.6.2-1 - Package Spec generated