#' @export
ContextualPrecachingBandit <- R6::R6Class(
inherit = Bandit,
class = FALSE,
public = list(
weights = NULL,
class_name = "ContextualPrecachingBandit",
initialize = function(weights) {
if (is.vector(weights)) {
self$weights <- matrix(weights, nrow = 1L)
} else {
self$weights <- weights
}
self$d <- nrow(self$weights)
self$k <- ncol(self$weights)
},
get_context = function(t) {
context <- list(k = self$k, d = self$d, X = private$X[,, t])
},
get_reward = function(t, context, action) {
reward <- list(
reward = private$R[action$choice, t],
optimal_reward = as.double(private$R[which_max_tied(private$R[, t]), t]),
optimal_arm = which_max_tied(private$R[, t])
)
},
generate_bandit_data = function(n = 1L) {
private$generate_contexts(n)
private$generate_rewards(n)
}
),
private = list(
R = NULL,
X = NULL,
W = NULL,
generate_contexts = function(n = 1L) {
private$X <- array(sample(c(0, 1), replace = TRUE, size = self$d * n), dim = c(self$d, self$k, n))
},
generate_rewards = function(n) {
weight_array <- array(t(matrix(self$weights, self$k , self$d, byrow = TRUE)),
dim = c(self$d, self$k, n))
rewards <- colSums(private$X*weight_array)
rewards <- rewards / colSums(private$X)
rewards[is.nan(rewards)] <- 0
private$W <- rewards
private$R <- round((runif( self$k * n) + rewards) / 2)
}
)
)
#' Bandit: ContextualPrecachingBandit
#'
#' Illustrates precaching of contexts and rewards.
#'
#' TODO: Fix "attempt to select more than one element in integerOneIndex"
#'
#' Contextual extension of \code{\link{BasicBernoulliBandit}}.
#'
#' Contextual extension of \code{\link{BasicBernoulliBandit}} where a user specified \code{d x k} dimensional
#' matrix takes the place of \code{\link{BasicBernoulliBandit}} \code{k} dimensional probability vector. Here,
#' each row \code{d} represents a feature with \code{k} reward probability values per arm.
#'
#' For every \code{t}, \code{ContextualPrecachingBandit} randomly samples from its \code{d} features/rows at
#' random, yielding a binary \code{context} matrix representing sampled (all 1 rows) and unsampled (all 0)
#' features/rows. Next, \code{ContextualPrecachingBandit} generates \code{rewards} contingent on either sum or
#' mean (default) probabilities of each arm/column over all of the sampled features/rows.
#'
#' @name ContextualPrecachingBandit
#'
#' @section Usage:
#' \preformatted{
#' bandit <- ContextualPrecachingBandit$new(weights)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#' \item{\code{weights}}{
#' numeric matrix; \code{d x k} dimensional matrix where each row \code{d} represents a feature with
#' \code{k} reward probability values per arm.
#' }
#' }
#'
#' @section Methods:
#'
#' \describe{
#'
#' \item{\code{new(weights)}}{ generates
#' and instantializes a new \code{ContextualPrecachingBandit} instance. }
#'
#' \item{\code{get_context(t)}}{
#' argument:
#' \itemize{
#' \item \code{t}: integer, time step \code{t}.
#' }
#' returns a named \code{list}
#' containing the current \code{d x k} dimensional matrix \code{context$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{generate_bandit_data()}}{
#' helper function called before \code{Simulator} starts iterating over all time steps \code{t} in T.
#' Pregenerates \code{contexts} and \code{rewards}.
#' }
#' }
#'
#' @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 <- 100L
#' simulations <- 100L
#'
#' # rows represent features, columns represent arms:
#'
#' context_weights <- matrix( c(0.4, 0.2, 0.4,
#' 0.3, 0.4, 0.3,
#' 0.1, 0.8, 0.1), nrow = 3, ncol = 3, byrow = TRUE)
#'
#' bandit <- ContextualPrecachingBandit$new(weights)
#'
#' agents <- list( Agent$new(EpsilonGreedyPolicy$new(0.1), bandit),
#' Agent$new(LinUCBDisjointOptimizedPolicy$new(0.6), bandit))
#'
#' simulation <- Simulator$new(agents, horizon, simulations)
#' history <- simulation$run()
#'
#' plot(history, type = "cumulative")
#'
#' }
NULL
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