#' @export
OfflineLookupReplayEvaluatorBandit <- R6::R6Class(
inherit = Bandit,
class = FALSE,
private = list(
S = NULL,
oa = NULL,
or = NULL,
shared_lookup = NULL,
unique_lookup = NULL,
unique_col = NULL,
rows = NULL
),
public = list(
class_name = "OfflineLookupReplayEvaluatorBandit",
randomize = NULL,
initialize = function(offline_data, k, shared_lookup = NULL, unique_lookup = NULL, unique_col = NULL,
unique = NULL, shared = NULL, randomize = TRUE) {
self$k <- k
self$randomize <- randomize
private$S <- data_table_factors_to_numeric(offline_data)
if(!is.null(unique_lookup)) {
dim_u <- dim(unique_lookup)[2]-1
self$unique <- c(1:dim_u)
private$unique_lookup <- as.matrix(unique_lookup[,-1])
private$unique_col <- unique_col
} else {
dim_u <- 0
self$unique <- 0
private$shared_lookup <- NULL
}
if(!is.null(shared_lookup)) {
dim_s <- dim(shared_lookup)[2]-1
self$shared <- c((dim_u+1):(dim_s + dim_u))
private$shared_lookup <- t(as.matrix(shared_lookup[,-1]))
} else {
dim_s <- 0
self$shared <- 0
private$shared_lookup <- NULL
}
self$d <- dim_s + dim_u
private$oa <- "optimal_arm" %in% colnames(offline_data)
private$or <- "optimal_reward" %in% colnames(offline_data)
},
post_initialization = function() {
if(isTRUE(self$randomize)) private$S <- private$S[sample(nrow(private$S))]
private$rows <- nrow(private$S)
},
get_context = function(index) {
if(index > private$rows) return(NULL)
if (self$unique!=0) {
ulookup <- private$unique_lookup[private$S[[private$unique_col]][[index]],]
unique_matrix <- matrix(ulookup, ncol = self$k, nrow = length(ulookup))
} else {
unique_matrix <- NULL
}
all_matrix <- rbind(unique_matrix, private$shared_lookup)
context <- list(
k = self$k,
d = self$d,
unique = self$unique,
shared = self$shared,
X = all_matrix
)
context
},
get_reward = function(index, context, action) {
if (private$S$choice[[index]] == action$choice) {
list(
reward = as.double(private$S$reward[[index]]),
optimal_reward = ifelse(private$or,
as.double(private$S$optimal_reward[[index]]),
NA),
optimal_arm = ifelse(private$oa,
as.double(private$S$optimal_arm[[index]]),
NA)
)
} else {
NULL
}
}
)
)
#' Bandit: Offline Replay with lookup tables
#'
#' Alternative interface for replay style bandit.
#'
#' TODO: Needs to be documented more fully.
#'
#' @name OfflineLookupReplayEvaluatorBandit
#'
#' @section Usage:
#' \preformatted{
#' bandit <- OfflineLookupReplayEvaluatorBandit(offline_data, k, shared_lookup = NULL, unique_lookup = NULL,
#' unique_col = NULL, unique = NULL, shared = NULL, randomize = TRUE)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#' \item{\code{offline_data}}{
#' data.table; offline data source (required)
#' }
#' \item{\code{k}}{
#' integer; number of arms (required)
#' }
#' \item{\code{d}}{
#' integer; number of contextual features (required)
#' }
#' \item{\code{randomize}}{
#' logical; randomize rows of data stream per simulation (optional, default: TRUE)
#' }
#' \item{\code{unique}}{
#' integer vector; index of disjoint features (optional)
#' }
#' \item{\code{shared}}{
#' integer vector; index of shared features (optional)
#' }
#'
#' }
#'
#' @section Methods:
#'
#' \describe{
#'
#' \item{\code{new(offline_data, k, shared_lookup = NULL, unique_lookup = NULL,
#' unique_col = NULL, unique = NULL, shared = NULL, randomize = TRUE)}}{ generates
#' and instantializes a new \code{OfflineLookupReplayEvaluatorBandit} instance. }
#'
#' \item{\code{get_context(t)}}{
#' argument:
#' \itemize{
#' \item \code{t}: integer, time step \code{t}.
#' }
#' returns a named \code{list}
#' containing the current \code{d x k} dimensional matrix \code{context$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()}}{
#' Randomize offline data by shuffling the offline data.table before the start of each
#' individual simulation when self$randomize is TRUE (default)
#' }
#' }
#'
#' @references
#'
#' Agrawal, R. (1995). The continuum-armed bandit problem. SIAM journal on control and optimization, 33(6),
#' 1926-1951.
#'
#' @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{OfflineLookupReplayEvaluatorBandit}}
#'
#' Policy subclass examples: \code{\link{EpsilonGreedyPolicy}}, \code{\link{ContextualLinTSPolicy}}
#'
#' @examples
#' \dontrun{
#'
#' library(contextual)
#' library(data.table)
#' library(splitstackshape)
#' library(RCurl)
#'
#' # Import MovieLens ml-10M
#'
#' # Info: https://d1ie9wlkzugsxr.cloudfront.net/data_movielens/ml-10M/README.html
#'
#' movies_dat <- "http://d1ie9wlkzugsxr.cloudfront.net/data_movielens/ml-10M/movies.dat"
#' ratings_dat <- "http://d1ie9wlkzugsxr.cloudfront.net/data_movielens/ml-10M/ratings.dat"
#'
#' movies_dat <- readLines(movies_dat)
#' movies_dat <- gsub( "::", "~", movies_dat )
#' movies_dat <- paste0(movies_dat, collapse = "\n")
#' movies_dat <- fread(movies_dat, sep = "~", quote="")
#' setnames(movies_dat, c("V1", "V2", "V3"), c("MovieID", "Name", "Type"))
#' movies_dat <- splitstackshape::cSplit_e(movies_dat, "Type", sep = "|", type = "character",
#' fill = 0, drop = TRUE)
#' movies_dat[[3]] <- NULL
#'
#' ratings_dat <- RCurl::getURL(ratings_dat)
#' ratings_dat <- readLines(textConnection(ratings_dat))
#' ratings_dat <- gsub( "::", "~", ratings_dat )
#' ratings_dat <- paste0(ratings_dat, collapse = "\n")
#' ratings_dat <- fread(ratings_dat, sep = "~", quote="")
#' setnames(ratings_dat, c("V1", "V2", "V3", "V4"), c("UserID", "MovieID", "Rating", "Timestamp"))
#'
#' all_movies <- ratings_dat[movies_dat, on=c(MovieID = "MovieID")]
#'
#' all_movies <- na.omit(all_movies,cols=c("MovieID", "UserID"))
#'
#' rm(movies_dat,ratings_dat)
#'
#' all_movies[, UserID := as.numeric(as.factor(UserID))]
#'
#' count_movies <- all_movies[,.(MovieCount = .N), by = MovieID]
#' top_50 <- as.vector(count_movies[order(-MovieCount)][1:50]$MovieID)
#' not_50 <- as.vector(count_movies[order(-MovieCount)][51:nrow(count_movies)]$MovieID)
#'
#' top_50_movies <- all_movies[MovieID %in% top_50]
#'
#' # Create feature lookup tables - to speed up, MovieID and UserID are
#' # ordered and lined up with the (dt/matrix) default index.
#'
#' # Arm features
#'
#' # MovieID of top 50 ordered from 1 to N:
#' top_50_movies[, MovieID := as.numeric(as.factor(MovieID))]
#' arm_features <- top_50_movies[,head(.SD, 1),by = MovieID][,c(1,6:24)]
#' setorder(arm_features,MovieID)
#'
#' # User features
#'
#' # Count of categories for non-top-50 movies normalized per user
#' user_features <- all_movies[MovieID %in% not_50]
#' user_features[, c("MovieID", "Rating", "Timestamp", "Name"):=NULL]
#' user_features <- user_features[, lapply(.SD, sum, na.rm=TRUE), by=UserID ]
#' user_features[, total := rowSums(.SD, na.rm = TRUE), .SDcols = 2:20]
#' user_features[, 2:20 := lapply(.SD, function(x) x/total), .SDcols = 2:20]
#' user_features$total <- NULL
#'
#' # Add users that were not in the set of non-top-50 movies (4 in 10m dataset)
#' all_users <- as.data.table(unique(all_movies$UserID))
#' user_features <- user_features[all_users, on=c(UserID = "V1")]
#' user_features[is.na(user_features)] <- 0
#'
#' setorder(user_features,UserID)
#'
#' rm(all_movies, not_50, top_50, count_movies)
#'
#' # Contextual format
#'
#' top_50_movies[, t := .I]
#' top_50_movies[, sim := 1]
#' top_50_movies[, agent := "Offline"]
#' top_50_movies[, choice := MovieID]
#' top_50_movies[, reward := ifelse(Rating <= 4, 0, 1)]
#'
#' setorder(top_50_movies,Timestamp,Name)
#'
#'
#' # Run simulation
#'
#' simulations <- 1
#' horizon <- nrow(top_50_movies)
#'
#' bandit <- OfflineLookupReplayEvaluatorBandit$new(top_50_movies,
#' k = 50,
#' unique_col = "UserID",
#' shared_lookup = arm_features,
#' unique_lookup = user_features)
#' agents <-
#' list(Agent$new(ThompsonSamplingPolicy$new(), bandit, "Thompson"),
#' Agent$new(UCB1Policy$new(), bandit, "UCB1"),
#' Agent$new(RandomPolicy$new(), bandit, "Random"),
#' Agent$new(LinUCBHybridOptimizedPolicy$new(0.9), bandit, "LinUCB Hyb 0.9"),
#' Agent$new(LinUCBDisjointOptimizedPolicy$new(2.1), bandit, "LinUCB Dis 2.1"))
#'
#' simulation <-
#' Simulator$new(
#' agents = agents,
#' simulations = simulations,
#' horizon = horizon
#' )
#'
#' results <- simulation$run()
#'
#' plot(results, type = "cumulative", regret = FALSE,
#' rate = TRUE, legend_position = "topleft")
#'
#'
#' }
NULL
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