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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(contextual) library(data.table) # Import personalization data-set library(contextual); library(data.table) dt <- fread("http://d1ie9wlkzugsxr.cloudfront.net/data_cmab_basic/data.txt") # 0/1 reward, 10 arms, 100 features # arms always start from 1 # z y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 .. x100 # 1: 2 0 5 0 0 37 6 0 0 0 0 25 0 0 7 1 0 .. 0 # 2: 8 0 1 3 36 0 0 0 0 0 0 0 0 1 0 0 0 .. 10 # 3: . . . . . . . . . . . . . . . . . .. . horizon <- nrow(dt) simulations <- 1 # Set up formula: y ~ z | x1 + x2 + .. # In bandit parlance: reward ~ arms | covariates or contextual features f <- y ~ z | . - z # Instantiate Replay Bandit (Li, 2010) bandit <- OfflineReplayEvaluatorBandit$new(formula = f, data = dt) # Bind Policies withs Bandits through Agents, add Agents to list 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")) # Instantiate a Simulator simulation <- Simulator$new(agents, horizon = nrow(dt), simulations = 1) # Run the simulation. history <- simulation$run() # plot the results plot(history, type = "cumulative", regret = FALSE, rate = TRUE, legend_position = "bottomright", ylim = c(0,1))
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