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policy_pooled_thompson.R
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policy_pooled_thompson.R
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library("rstan")
UnpooledThompsonPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "UnpooledThompsonPolicy",
n_subjects = NULL,
initialize = function(n_subjects = 1) {
super$initialize()
self$n_subjects <- n_subjects
},
set_parameters = function(context_params) {
self$theta <- list(p = rep(list(list(0,0)),self$n_subjects),
n = rep(list(list(0,0)),self$n_subjects))
},
get_action = function(t, context) {
user <- context$user_context
expected_rewards <- rep(0.0, context$k)
for (arm in 1:context$k) {
a <- theta$p[[user]][[arm]] * theta$n[[user]][[arm]]
b <- theta$n[[user]][[arm]] - a
if (a == 0 || b == 0) {
expected_rewards[arm] = rbeta(1,1,1)
} else {
expected_rewards[arm] = rbeta(1,a,b)
}
}
action$choice <- which_max_tied(expected_rewards)
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
user <- context$user_context
reward <- reward$reward
inc(self$theta$n[[user]][[arm]]) <- 1
inc(self$theta$p[[user]][[arm]]) <- (reward - self$theta$p[[user]][[arm]]) / self$theta$n[[user]][[arm]]
self$theta
}
)
)
PooledThompsonPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "PooledThompsonPolicy",
n_subjects = NULL,
initialize = function() {
super$initialize()
},
set_parameters = function(context_params) {
self$theta_to_arms <- list('N' = 0, 'P' = 0)
},
get_action = function(t, context) {
expected_rewards <- rep(0.0, context$k)
for (arm in 1:context$k) {
a <- self$theta$P[[arm]] * self$theta$N[[arm]]
b <- self$theta$N[[arm]] - a
if (a == 0 || b == 0) {
expected_rewards[arm] = rbeta(1,1,1)
} else {
expected_rewards[arm] = rbeta(1,a,b)
}
}
action$choice <- which_max_tied(expected_rewards)
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
reward <- reward$reward
inc(self$theta$N[[arm]]) <- 1
inc(self$theta$P[[arm]]) <- (reward - self$theta$P[[arm]]) / self$theta$N[[arm]]
self$theta
}
)
)
PartiallyPooledThompsonPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
n_subjects = NULL,
theta_a = NULL,
theta_b = NULL,
theta_samples_a = NULL,
theta_samples_b = NULL,
stan_model = NULL,
warm_up = NULL,
iter = NULL,
class_name = "PartiallyPooledThompsonPolicy",
initialize = function(n_subjects = 1,
stan_model,
warm_up = 10,
iter = 20) {
super$initialize()
self$n_subjects = n_subjects
self$stan_model = stan_model
self$warm_up = warm_up
self$iter = iter
},
set_parameters = function(context_params) {
self$theta <- list(n = list(rep(0,self$n_subjects),rep(0,self$n_subjects)), # TODO: make this into k-arms 0, not 0,0
l = list(rep(0,self$n_subjects),rep(0,self$n_subjects))) # TODO: make this into k-arms 0, not 0,0
self$theta_to_arms <- list('N' = 0)
n_subjects <- self$n_subjects
n <- self$theta$n[[1]]
l <- self$theta$l[[1]]
capture.output(fit_a <- rstan::sampling(self$stan_model,
data = c("n_subjects", "n", "l"),
iter = self$iter,
warmup = self$warm_up,
refresh = 0,
chains = 1))
self$theta_a <- rstan::summary(fit_a, pars = c("theta"))$summary[, "mean"]
n_subjects <- self$n_subjects
n <- self$theta$n[[2]]
l <- self$theta$l[[2]]
capture.output(fit_b <- rstan::sampling(self$stan_model,
data = c("n_subjects", "n", "l"),
iter = self$iter,
warmup = self$warm_up,
refresh = 0,
chains = 1))
self$theta_b <- summary(fit_b, pars = c("theta"))$summary[, "mean"]
self$theta_samples_a <- extract(fit_a, pars = c("theta"))$theta
self$theta_samples_b <- extract(fit_b, pars = c("theta"))$theta
},
get_action = function(t, context) {
userid <- context$user_context
if (self$theta_samples_a[theta$n[[1]][userid] %% 10 + 1, userid] > self$theta_samples_b[theta$n[[2]][userid] %% 10 + 1, userid]) {
action$choice = 1
} else {
action$choice = 2
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
userid <- context$user_context
reward <- reward$reward
if (arm == 1) {
self$theta$n[[1]][userid] <- self$theta$n[[1]][userid] + 1
self$theta$l[[1]][userid] <- self$theta$l[[1]][userid] + reward
self$theta$N[[1]] <- self$theta$N[[1]] + 1
if (self$theta$N[[1]] %% 10 == 0) {
init_val_a <- list(list(theta = self$theta_a))
n_subjects <- self$n_subjects
n <- self$theta$n[[1]]
l <- self$theta$l[[1]]
capture.output(fit_a <- rstan::sampling(self$stan_model,
data = c("n_subjects", "n", "l"),
iter = self$iter ,
init = init_val_a,
warmup = self$warm_up,
refresh = 0,
chains = 1))
self$theta_a <- rstan::summary(fit_a, pars = c("theta"))$summary[, "mean"]
self$theta_samples_a <- rstan::extract(fit_a, pars = c("theta"))$theta
}
} else {
self$theta$n[[2]][userid] <- self$theta$n[[2]][userid] + 1
self$theta$l[[2]][userid] <- self$theta$l[[2]][userid] + reward
self$theta$N[[2]] <- self$theta$N[[2]] + 1
if (self$theta$N[[2]] %% 10 == 0) {
init_val_b <-
list(list(
theta = self$theta_b
))
n_subjects <- self$n_subjects
n <- self$theta$n[[2]]
l <- self$theta$l[[2]]
capture.output(fit_b <- rstan::sampling(self$stan_model,
data = c("n_subjects", "n", "l"),
iter = self$iter ,
init = init_val_b,
warmup = self$warm_up,
refresh = 0,
chains = 1))
self$theta_b <- rstan::summary(fit_b, pars = c("theta"))$summary[, "mean"]
self$theta_samples_b <- rstan::extract(fit_b, pars = c("theta"))$theta
}
}
self$theta
}
)
)