#' @export
ContextualLogitBandit <- R6::R6Class(
"ContextualLogitBandit",
inherit = Bandit,
class = FALSE,
public = list(
rewards = NULL,
beta = NULL,
intercept = NULL,
class_name = "ContextualLogitBandit",
initialize = function(k, d, intercept = TRUE) {
self$k <- k
self$d <- d
self$intercept <- intercept
},
post_initialization = function() {
if (self$intercept && self$d > 1) {
self$beta <- c(rnorm(self$d-1,0,1),1)
} else {
self$beta <- c(rnorm(self$d,0,1))
}
},
get_context = function(t) {
X <- matrix(runif(self$d*self$k, 0, 1), self$d, self$k)
context <- list(
k = self$k,
d = self$d,
X = X
)
},
get_reward = function(t, context, action) {
X <- context$X # context matrix
d <- context$d # number of context features
arm <- action$choice # arm chosen by policy
z <- as.vector(self$beta%*%X) # compute linear predictor
pr <- 1/(1+exp(-z)) # inverse logit transform of linear predictor
rewards <- rbinom(d,1,pr) # binary rewards from the Bernoulli distribution
optimal_arm <- which_max_tied(pr)
reward <- list(
reward = rewards[action$choice],
optimal_arm = optimal_arm,
optimal_reward = rewards[optimal_arm]
)
}
)
)
#' Bandit: ContextualLogitBandit
#'
#' Samples data from a basic logistic regression model.
#'
#' ContextualLogitBandit linear predictors are generated from the dot product of a random \code{d} dimensional
#' normal weight vector and uniform random \code{d x k} dimensional context matrices with equal weights per
#' arm. This product is then inverse-logit transformed to generate \code{k} dimensional binary (0/1) reward
#' vectors by randomly sampling from a Bernoulli distribution.
#'
#' @name ContextualLogitBandit
#'
#' @section Usage:
#' \preformatted{
#' bandit <- ContextualLogitBandit$new(k, d, intercept = TRUE)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#'
#' \item{\code{k}}{
#' integer; number of bandit arms
#' }
#' \item{\code{d}}{
#' integer; number of contextual features
#' }
#' \item{\code{intercept}}{
#' logical; if TRUE (default) it adds a constant (1.0) dimension to each context X at the end.
#' }
#'
#' }
#'
#' @section Methods:
#'
#' \describe{
#'
#' \item{\code{new(k, d, intercept = TRUE)}}{ generates and instantializes a new
#' \code{ContextualLogitBandit} instance. }
#'
#' \item{\code{get_context(t)}}{
#' argument:
#' \itemize{
#' \item \code{t}: integer, time step \code{t}.
#' }
#' returns a named \code{list}
#' containing the current \code{d x k} dimensional matrix \code{context$X},
#' the number of arms \code{context$k} and the number of features \code{context$d}.
#' }
#'
#' \item{\code{get_reward(t, context, action)}}{
#' arguments:
#' \itemize{
#' \item \code{t}: integer, time step \code{t}.
#' \item \code{context}: list, containing the current \code{context$X} (d x k context matrix),
#' \code{context$k} (number of arms) and \code{context$d} (number of context features)
#' (as set by \code{bandit}).
#' \item \code{action}: list, containing \code{action$choice} (as set by \code{policy}).
#' }
#' returns a named \code{list} containing \code{reward$reward} and, where computable,
#' \code{reward$optimal} (used by "oracle" policies and to calculate regret).
#' }
#'
#' \item{\code{post_initialization()}}{
#' initializes \code{d x k} beta matrix.
#' }
#
#' }
#'
#' @seealso
#'
#' Core contextual classes: \code{\link{Bandit}}, \code{\link{Policy}}, \code{\link{Simulator}},
#' \code{\link{Agent}}, \code{\link{History}}, \code{\link{Plot}}
#'
#' Bandit subclass examples: \code{\link{BasicBernoulliBandit}}, \code{\link{ContextualLogitBandit}},
#' \code{\link{OfflineReplayEvaluatorBandit}}
#'
#' Policy subclass examples: \code{\link{EpsilonGreedyPolicy}}, \code{\link{ContextualLinTSPolicy}}
#'
#' @examples
#' \dontrun{
#'
#' horizon <- 800L
#' simulations <- 30L
#'
#' bandit <- ContextualLogitBandit$new(k = 5, d = 5, intercept = TRUE)
#'
#' agents <- list(Agent$new(ContextualLinTSPolicy$new(0.1), bandit),
#' Agent$new(EpsilonGreedyPolicy$new(0.1), bandit),
#' Agent$new(LinUCBGeneralPolicy$new(0.6), bandit),
#' Agent$new(ContextualEpochGreedyPolicy$new(8), bandit),
#' Agent$new(LinUCBHybridOptimizedPolicy$new(0.6), bandit),
#' Agent$new(LinUCBDisjointOptimizedPolicy$new(0.6), bandit))
#'
#' simulation <- Simulator$new(agents, horizon, simulations)
#' history <- simulation$run()
#'
#' plot(history, type = "cumulative", regret = FALSE,
#' rate = TRUE, legend_position = "right")
#' }
NULL
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