library(contextual)
library(data.table)
library(Formula)
# Import personalization data-set
data <- fread("http://d1ie9wlkzugsxr.cloudfront.net/data_cmab_basic/data.txt")
# 0/1 reward, 10 arms, 100 features
# arms always start from 1
# z y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 .. x100
# 1: 2 0 5 0 0 37 6 0 0 0 0 25 0 0 7 1 0 .. 0
# 2: 8 0 1 3 36 0 0 0 0 0 0 0 0 1 0 0 0 .. 10
# 3: . . . . . . . . . . . . . . . . . .. .
simulations <- 1
horizon <- nrow(data)
# Run regression per arm, predict outcomes, and save results, a column per arm
x <- reformulate(names(data)[3:102],response="y") # x: x1 .. x100
f <- Formula::as.Formula(x) # y ~ x
model_f <- function(arm) glm(f, data=data[z==arm], family=binomial(link="logit"), y=F, model=F)
arms <- sort(unique(data$z))
model_arms <- lapply(arms, FUN = model_f)
predict_arm <- function(model) predict(model, data, type = "response")
r_data <- lapply(model_arms, FUN = predict_arm)
r_data <- do.call(cbind, r_data)
colnames(r_data) <- paste0("r", (1:max(arms)))
# Bind data and model predictions
data <- cbind(data,r_data)
# Run direct method style offline bandit
x <- reformulate(names(data)[3:102], response="y")
z <- ~ z
r <- ~ r1 + r2 + r3 + r4 + r5 + r6 + r7 + r8 + r9 + r10
f <- as.Formula(z,x,r) # Resulting in: y ~ z | x1 + x2 .. | r1 + r2 + ..
bandit <- OfflineDirectMethodBandit$new(formula = f, data = data)
# Define agents.
agents <- list(Agent$new(LinUCBDisjointOptimizedPolicy$new(0.01), bandit, "alpha = 0.01"),
Agent$new(LinUCBDisjointOptimizedPolicy$new(0.05), bandit, "alpha = 0.05"),
Agent$new(LinUCBDisjointOptimizedPolicy$new(0.1), bandit, "alpha = 0.1"),
Agent$new(LinUCBDisjointOptimizedPolicy$new(1.0), bandit, "alpha = 1.0"))
# 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", ylim = c(0,1))
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