Minor reproducibility update

Nth-iteration-labs · Jul 29, 2020 · 2eb4122 · 2eb4122
1 parent 2dc61a6
commit 2eb4122
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Showing 3 changed files with 25 additions and 37 deletions.
diff --git a/1_offline_ranking.R b/1_offline_ranking.R
@@ -130,21 +130,4 @@ for(i in 1:length(deltas)){
 p <- plots[[1]] + plots[[2]] + plots[[3]] + plots[[4]] + plots[[5]] + plots[[6]] + plot_layout(guides = "collect") & theme(legend.position = "bottom")
 ggsave(paste0(f[[1]],"_offline.pdf"), p, device="pdf", width=10, height=7)
 
-#pdf(paste0("offline_",f[[1]],".pdf"))
-
-#layout(matrix(c(1,2,3,4,5,6,7,7,7), ncol=3, byrow=TRUE), heights=c(3, 3, 1))
-#par(oma=c(4,4,2,2), las=1)
-##par(cex=1.05)
-#for(hist in histories){
-#    plot(hist, type="cumulative", no_par = TRUE, disp="ci", legend=FALSE, use_colors=TRUE, trunc_per_agent = FALSE, xlab = "", ylab = "")
-#}
-#par(cex=1.05)
-#mtext("Time step", 1, 3, outer=TRUE, las = 0)
-#mtext("Cumulative regret", 2, 3, outer=TRUE, las = 0)
-#par(mar=c(1,1,1,1))
-#plot.new()
-#legend(x="center", ncol=4,legend=c("UR","E-First","LiF", "TBL"), fill=c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF"), title="Legend")
-#
-#dev.off()
 }
-
diff --git a/bandit_continuum_offon.R b/bandit_continuum_offon.R
@@ -21,8 +21,12 @@ OnlineOfflineContinuumBandit <- R6::R6Class(
     post_initialization = function() {
       self$choice <- runif(self$horizon, min=0, max=1)
       temp_data <- self$arm_function(self$choice)
-      self$S <- data.frame(self$choice, temp_data$data)
-      self$maxval <- temp_data$max
+      if(self$max_bool == TRUE){
+          self$S <- data.frame(self$choice, temp_data$data)
+          self$maxval <- temp_data$max
+      } else {
+          self$S <- data.frame(self$choice, temp_data)
+      }
       self$S <- self$S[sample(nrow(self$S)),]
       colnames(self$S) <- c('choice', 'reward')
     },

diff --git a/bandit_continuum_offon_kern.R b/bandit_continuum_offon_kern.R
@@ -2,49 +2,50 @@
 OnlineOfflineContinuumBanditKernel <- R6::R6Class(
   inherit = Bandit,
   class = FALSE,
-  private = list(
-    S = NULL,
-    n = NULL
-  ),
   public = list(
     class_name = "OnlineOfflineContinuumBanditKernel",
-    delta = NULL,
-    c1 = NULL,
-    c2 = NULL,
     arm_function = NULL,
     choice = NULL,
     h = NULL,
     kernel = NULL,
     horizon = NULL,
-    initialize   = function(FUN, horizon) {
+    max_bool = FALSE,
+    maxval = NULL,
+    S = NULL,
+    n = NULL,
+    initialize   = function(FUN, max_bool, horizon) {
       self$arm_function <- FUN
       self$k <- 1
       self$horizon <- horizon
       self$h <- horizon^(-1/5)
       self$kernel <- function(action_true, action_choice, bandwith){ 1/sqrt(2*pi)*exp(((action_choice - action_true) / bandwith)^2/2) }
+      self$max_bool <- max_bool
     },
     post_initialization = function() {
       self$choice <- runif(self$horizon, min=0, max=1)
-      private$S <- data.frame(self$choice, self$arm_function(self$choice))
-      private$S <- private$S[sample(nrow(private$S)),]
-      colnames(private$S) <- c('choice', 'reward')
-      private$n <- 0
+      temp_data <- self$arm_function(self$choice)
+      if(self$max_bool == TRUE){
+          self$S <- data.frame(self$choice, temp_data$data)
+          self$maxval <- temp_data$max
+      } else {
+          self$S <- data.frame(self$choice, temp_data)
+      }
+      self$S <- self$S[sample(nrow(self$S)),]
+      colnames(self$S) <- c('choice', 'reward')
+      self$n <- 0
     },
     get_context = function(index) {
       context           <- list()
       context$k         <- self$k
       context
     },
     get_reward = function(index, context, action) {
-      reward_at_index <- as.double(private$S$reward[[index]])
-      #kern_value <- self$kernel(action_true = private$S$choice[[index]], action_choice = action$choice, bandwith = self$h)
-      temp_u <- (action$choice - private$S$choice[[index]]) / self$h
+      reward_at_index <- as.double(self$S$reward[[index]])
+      temp_u <- (action$choice - self$S$choice[[index]]) / self$h
       kern_value <- 1/sqrt(2*pi) * exp(-temp_u^2 / 2)
-      #inc(private$n) <- 1
-      #print(paste0("Kernel value: ", kern_value, "action choice: ", action$choice, "true action: ", private$S$choice[[index]], "divy: ", temp_u))
       reward <- list(
         reward = (kern_value * reward_at_index),
-        optimal_reward = self$c2
+        optimal_reward = ifelse(self$max_bool, self$maxval, NA)
       )
     }
   )