Nothing
#' @export
OfflineDoublyRobustBandit <- R6::R6Class(
inherit = OfflineBootstrappedReplayBandit,
class = FALSE,
private = list(
r = NULL,
p = NULL,
p_hat = NULL,
n = NULL
),
public = list(
threshold = NULL,
class_name = "OfflineDoublyRobustBandit",
initialize = function(formula,
data, k = NULL, d = NULL,
unique = NULL, shared = NULL,
threshold = 0,
randomize = TRUE) {
self$threshold <- threshold
super$initialize(formula,
data, k, d,
unique, shared,
randomize, replacement = FALSE,
jitter = FALSE, arm_multiply = FALSE)
},
post_initialization = function() {
super$post_initialization()
# modeled reward for each arm at each t
private$r <- model.matrix(private$formula, data = private$S, rhs = 3)[,-1]
private$p <- Formula::model.part(private$formula, data = private$S, lhs = 0, rhs = 4, drop = TRUE)
if (length(private$p) == 0 || is.null(private$p)) {
marginal_prob <- table(private$z)/length(private$z)
private$p <- marginal_prob[private$z]
}
private$n <- 0
private$p_hat <- 0
},
get_reward = function(index, context, action) {
choice <- action$choice
data_reward <- private$y[index]
model_reward <- private$r[index,choice]
p <- private$p[index]
indicator <- ind(private$z[index] == choice)
if (indicator) {
p <- max(private$p[index], self$threshold) # when threshold 0 (default)
# p = private$p[index]
w <- 1 / p
prop_reward <- (data_reward - model_reward) * w
} else {
prop_reward <- 0
}
robust_reward <- prop_reward + model_reward
list(
reward = as.double(robust_reward),
optimal_reward = ifelse(private$or, as.double(private$S$optimal_reward[[index]]), NA),
optimal_arm = ifelse(private$oa, as.double(private$S$optimal_arm[[index]]), NA)
)
}
)
)
#' Bandit: Offline Doubly Robust
#'
#' Bandit for the doubly robust evaluation of policies with offline data.
#'
#' @name OfflineDoublyRobustBandit
#'
#' @section Usage:
#' \preformatted{
#' bandit <- OfflineDoublyRobustBandit(formula,
#' data, k = NULL, d = NULL,
#' unique = NULL, shared = NULL,
#' randomize = TRUE)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#' \item{\code{formula}}{
#' formula (required).
#' Format:
#' \code{y.context ~ z.choice | x1.context + x2.xontext + ... | r1.reward + r2.reward ... | p.propensity}
#' Here, r1.reward to rk.reward represent regression based precalculated rewards per arm.
#' When leaving out p.propensity, Doubly Robust Bandit uses marginal prob per arm for propensities:
# table(private$z)/length(private$z).
#' Adds an intercept to the context model by default. Exclude the intercept, by adding "0" or "-1" to
#' the list of contextual features, as in: \code{y.context ~ z.choice | x1.context + x2.xontext -1}
#' }
#' \item{\code{data}}{
#' data.table or data.frame; offline data source (required)
#' }
#' \item{\code{k}}{
#' integer; number of arms (optional). Optionally used to reformat the formula defined x.context vector
#' as a \code{k x d} matrix. When making use of such matrix formatted contexts, you need to define custom
#' intercept(s) when and where needed in data.table or data.frame.
#' }
#' \item{\code{d}}{
#' integer; number of contextual features (optional) Optionally used to reformat the formula defined
#' x.context vector as a \code{k x d} matrix. When making use of such matrix formatted contexts, you need
#' to define custom intercept(s) when and where needed in data.table or data.frame.
#' }
#' \item{\code{randomize}}{
#' logical; randomize rows of data stream per simulation (optional, default: TRUE)
#' }
#' \item{\code{replacement}}{
#' logical; sample with replacement (optional, default: FALSE)
#' }
#' \item{\code{jitter}}{
#' logical; add jitter to contextual features (optional, default: FALSE)
#' }
#' \item{\code{unique}}{
#' integer vector; index of disjoint features (optional)
#' }
#' \item{\code{shared}}{
#' integer vector; index of shared features (optional)
#' }
#' \item{\code{threshold}}{
#' float (0,1); Lower threshold or Tau on propensity score values. Smaller Tau makes for less biased
#' estimates with more variance, and vice versa. For more information, see paper by Strehl at all (2010).
#' Values between 0.01 and 0.05 are known to work well.
#' }
#'
#' }
#'
#' @section Methods:
#'
#' \describe{
#'
#' \item{\code{new(formula, data, k = NULL, d = NULL, unique = NULL, shared = NULL, randomize = TRUE)}}{
#' generates and instantializes a new \code{OfflineDoublyRobustBandit} 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$randomize is TRUE (default)
#' }
#' }
#'
#' @references
#'
#' DudÃk, Miroslav, John Langford, and Lihong Li. "Doubly robust policy evaluation and learning."
#' arXiv preprint arXiv:1103.4601 (2011).
#'
#' Agarwal, Alekh, et al. "Taming the monster: A fast and simple algorithm for contextual bandits."
#' International Conference on Machine Learning. 2014.
#'
#' Strehl, Alex, et al. "Learning from logged implicit exploration data." Advances in Neural Information
#' Processing Systems. 2010.
#'
#' @seealso
#'
#' Core contextual classes: \code{\link{Bandit}}, \code{\link{Policy}}, \code{\link{Simulator}},
#' \code{\link{Agent}}, \code{\link{History}}, \code{\link{Plot}}
#'
#' Bandit subclass examples: \code{\link{BasicBernoulliBandit}}, \code{\link{ContextualLogitBandit}},
#' \code{\link{OfflineDoublyRobustBandit}}
#'
#' Policy subclass examples: \code{\link{EpsilonGreedyPolicy}}, \code{\link{ContextualLinTSPolicy}}
#'
#' @examples
#' \dontrun{
#'
#' library(contextual)
#' ibrary(data.table)
#'
#' # Import myocardial infection dataset
#'
#' url <- "http://d1ie9wlkzugsxr.cloudfront.net/data_propensity/myocardial_propensity.csv"
#' data <- fread(url)
#'
#' simulations <- 300
#' horizon <- nrow(data)
#'
#' # arms always start at 1
#' data$trt <- data$trt + 1
#'
#' # turn death into alive, making it a reward
#' data$alive <- abs(data$death - 1)
#'
#' # Run regression per arm, predict outcomes, and save results, a column per arm
#'
#' f <- alive ~ age + risk + severity
#'
#' model_f <- function(arm) glm(f, data=data[trt==arm],
#' family=binomial(link="logit"),
#' y=FALSE, model=FALSE)
#' arms <- sort(unique(data$trt))
#' model_arms <- lapply(arms, FUN = model_f)
#'
#' predict_arm <- function(model) predict(model, data, type = "response")
#' r_data <- lapply(model_arms, FUN = predict_arm)
#' r_data <- do.call(cbind, r_data)
#' colnames(r_data) <- paste0("r", (1:max(arms)))
#'
#' # Bind data and model predictions
#'
#' data <- cbind(data,r_data)
#'
#' m <- glm(I(trt-1) ~ age + risk + severity, data=data, family=binomial(link="logit"))
#' data$p <-predict(m, type = "response")
#'
#' f <- alive ~ trt | age + risk + severity | r1 + r2 | p
#'
#' bandit <- OfflineDoublyRobustBandit$new(formula = f, data = data)
#'
#' # Define agents.
#' agents <- list(Agent$new(LinUCBDisjointOptimizedPolicy$new(0.2), bandit, "LinUCB"),
#' Agent$new(FixedPolicy$new(1), bandit, "Arm1"),
#' Agent$new(FixedPolicy$new(2), bandit, "Arm2"))
#'
#' # Initialize the simulation.
#'
#' simulation <- Simulator$new(agents = agents, simulations = simulations, horizon = horizon)
#'
#' # Run the simulation.
#' sim <- simulation$run()
#'
#' # plot the results
#' plot(sim, type = "cumulative", regret = FALSE, rate = TRUE, legend_position = "bottomright")
#'
#' plot(sim, type = "arms", limit_agents = "LinUCB")
#'
#' }
NULL
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