FinalSimulationCode/performancePlot_RR_xgboost.R

In Larsvanderlaan/npcausalML: Nonparametric estimation of the relative risk function

library(ggplot2)
library(data.table)
ns <- c(  5000,  500, 1000, 2500)
ns <- sort(ns)
hard_list <-  c(F)
pos_list <-  c(T)
use_oracle_sieve <- F
for(pos in pos_list){
  for(hard in hard_list) {
   ({
    sims_list <- lapply(ns, function(n) {
      try({load(paste0("mainSimResults/mainSimResults/simsLRR", hard, pos,  "n", n, "_xgboost"))})


      simresults <- get(paste0("simresults"))


      keep <- sapply(simresults, function(item) {
        try({
          force(item$risk_IPW)
          return(TRUE)
          })
        return(FALSE)
      })
      simresults <- simresults[keep]
      ipwrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_IPW")))

       substrisks  <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_subst")))



       lrnr_names <- names(simresults[[1]]$risk_IPW) #simresults[[1]]$sieve[[1]]
       lrnr_names[grep("CV",lrnr_names)] <- c("Lrnr_xgboost_cv", "Lrnr_rf_cv")
       lrnr_names_no_sieve <- lrnr_names
      lrnr_names <- unlist(lapply(lrnr_names, function(name) {
        paste0(name , c( "_no_sieve.plugin", paste0("_fourier_basis_", 1:4, "_plugin")))
      }))
      iter <- rep(1:length(simresults), each = length(lrnr_names))
      cvrisksDRoracle <- unlist( lapply(simresults, function(item) {
        item$sieve$cvrisksDRoracle
      }))

      cvrisksDR <- unlist(lapply(seq_along(simresults), function(index) {
        item <- simresults[[index]]
        as.vector(item$sieve$cvrisksDR)

      }))


      risks_oracle <- unlist( lapply(simresults, function(item) {
        item$sieve$risks_oracle
      }))

      dt <- data.table(iter, lrnr_full = lrnr_names, cvrisksDR, cvrisksDRoracle, risks_oracle)
      dt$degree <- as.numeric(stringr::str_match(dt$lrnr_full, "fourier_basis_([0-9]+)")[,2])
      dt$degree[grep("no_sieve", dt$lrnr_full)] <- 0

      dt$lrnr[ grep("gam3", dt$lrnr_full)] <- "gam3"
      dt$lrnr[ grep("gam4", dt$lrnr_full)] <- "gam4"
      dt$lrnr[ grep("gam5", dt$lrnr_full)] <- "gam5"
      dt$lrnr[ grep("glm", dt$lrnr_full)] <- "glm"
      dt$lrnr[ grep("earth", dt$lrnr_full)] <- "earth"
      dt$lrnr[ grep("earth", dt$lrnr_full)] <- "earth"
      dt$lrnr[ grep("rpart", dt$lrnr_full)] <- "rpart"
      dt$lrnr[ grep("ranger_500_TRUE_none_1_7", dt$lrnr_full)] <- "ranger_7"
      dt$lrnr[ grep("ranger_500_TRUE_none_1_13", dt$lrnr_full)] <- "ranger_13"
      dt$lrnr[ grep("ranger_500_TRUE_none_1_10", dt$lrnr_full)] <- "ranger_10"

      ## dt$lrnr[ grep("xgboost_20_1_7", dt$lrnr_full)] <- "xgboost_7"
      #  dt$lrnr[ grep("xgboost_20_1_5", dt$lrnr_full)] <- "xgboost_5"
      # dt$lrnr[ grep("xgboost_20_1_3", dt$lrnr_full)] <- "xgboost_3"
      dt$lrnr <- gsub("Lrnr_", "", dt$lrnr)
      dt$lrnr <- gsub("_fourier_basis.+", "", dt$lrnr)


      dt$type[!is.na(as.numeric(dt$degree))] <- "sieve"
      dt$type[is.na(as.numeric(dt$degree))] <- dt$degree[is.na(as.numeric(dt$degree))]

      dt <- dt[dt$degree > 0]

      tmp <- dt[, cv_sieve_risk := risks_oracle[which.min(cvrisksDR)], by = c("lrnr", "iter")]
      tmp <- tmp[, oracle_sieve_risk := risks_oracle[which.min(risks_oracle)], by = c("lrnr", "iter")]
      tmp <- tmp[!duplicated(paste0(degree, lrnr, iter )),]


       tmp <- dt[, cv_sieve_risk := which.min(cvrisksDR), by = c("lrnr", "iter")]
       tmp <- tmp[, oracle_sieve_risk := which.min(risks_oracle), by = c("lrnr", "iter")]
       tmp <- tmp[!duplicated(paste0(degree, lrnr, iter )),]



      ###### LATER


      #dt <- data.table(lrnr_full = lrnr_names, cvrisksDRoracle,cvrisksDR,  risks_oracle)
     # dt$degree <- as.numeric(stringr::str_match(dt$lrnr_full, "fourier_basis_([0-9]+)")[,2])
      #dt$degree[grep("no_sieve", dt$lrnr_full)] <- 0

      tmp <- data.table(risks_oracle = ipwrisks, lrnr_full = lrnr_names_no_sieve, lrnr = lrnr_names_no_sieve ,degree = "ipw")
      dt <- rbind(dt, tmp, fill = T)
      tmp <- data.table(risks_oracle = substrisks, lrnr_full = lrnr_names_no_sieve, degree = "subst")
      dt <- rbind(dt, tmp, fill = T)
      dt$lrnr <- dt$lrnr_full
      dt$lrnr[ grep("gam3", dt$lrnr_full)] <- "gam3"
      dt$lrnr[ grep("gam4", dt$lrnr_full)] <- "gam4"
      dt$lrnr[ grep("gam5", dt$lrnr_full)] <- "gam5"
      dt$lrnr[ grep("glm", dt$lrnr_full)] <- "glm"
      dt$lrnr[ grep("earth", dt$lrnr_full)] <- "earth"
      dt$lrnr[ grep("earth", dt$lrnr_full)] <- "earth"
      dt$lrnr[ grep("rpart", dt$lrnr_full)] <- "rpart"
      dt$lrnr[ grep("ranger_500_TRUE_none_1_7", dt$lrnr_full)] <- "ranger_7"
      dt$lrnr[ grep("ranger_500_TRUE_none_1_13", dt$lrnr_full)] <- "ranger_13"
      dt$lrnr[ grep("ranger_500_TRUE_none_1_10", dt$lrnr_full)] <- "ranger_10"

      ## dt$lrnr[ grep("xgboost_20_1_7", dt$lrnr_full)] <- "xgboost_7"
      #  dt$lrnr[ grep("xgboost_20_1_5", dt$lrnr_full)] <- "xgboost_5"
      # dt$lrnr[ grep("xgboost_20_1_3", dt$lrnr_full)] <- "xgboost_3"

      dt$lrnr <- gsub("Lrnr_", "", dt$lrnr)
      dt$lrnr <- gsub("_fourier_basis.+", "", dt$lrnr)

      dt$type[!is.na(as.numeric(dt$degree))] <- "sieve"
      dt$type[is.na(as.numeric(dt$degree))] <- dt$degree[is.na(as.numeric(dt$degree))]

      print(unique(dt$lrnr_full))

      if(!use_oracle_sieve){
        dt[!is.na(as.numeric(dt$degree)), risks_oracle := risks_oracle[which.min(cvrisksDR)], by = c("lrnr", "type", "iter")]

      } else {
        dt[!is.na(as.numeric(dt$degree)), risks_oracle := min(risks_oracle), by = c("iter", "lrnr", "type")]
      }
      dt[, risks_best := mean(risks_oracle), by = c("lrnr", "type")]

      #dt[is.na(as.numeric(dt$degree)), risks_best := risks_oracle, by = c("lrnr", "type")]

      dt2 <- dt[,c("lrnr", "risks_best", "type"), with = F]
      dt2 <- unique(dt2)
      dt2$n <- n
      return(dt2)
    })


    dt <- rbindlist(sims_list)
    #dt <- dt[dt$type != "subst"]
    #dt <- dt[dt$lrnr != "causalforest"]

    options(repr.plot.width=20, repr.plot.height=10)

    dt[(dt$type == "sieve"),"type"] <- "EP-learner (*)"
    dt[(dt$type == "ipw"),"type"] <- "IPW-learner"
    dt[(dt$type == "subst"),"type"] <- "T-learner"
    #dt <- dt[(dt$type != "xgboost-Ensemble"), ]

    dt <- dt[(dt$type != "xgboost-Ensemble"), ]
    # dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]


    dt_tmp<-dt
    dt_tmp <- dt_tmp[ grep("xgboost", dt$lrnr), ]
    max_depth <- stringr::str_match(dt_tmp$lrnr, "xgboost_10_1_([0-9]+)")[,2]
    max_depth[is.na(max_depth)] <- "cv"
    dt_tmp$lrnr <- paste0("xgboost (", "max_depth=", max_depth,")")
    dt_tmp <- dt_tmp[max_depth %in% c("1", "2", "4", "6", "cv"),]
    dt_tmp <- rbind(dt_tmp, dt[grep("causalforest", dt$lrnr),])
    dt_xg <- dt_tmp

    plt <- ggplot(dt_tmp, aes(x = n, y = risks_best, group = type, color = type, linetype = type)) + geom_line() +
      facet_wrap(~lrnr, scales = "free") + theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) + ylab("MSE") + scale_y_log10(limits = range(dt_tmp$risks_best))  +  scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
    plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw() + labs(color = "Method", group = "Method", linetype = "Method")
    ggsave(paste0("mainSimResults/performancePlot_xgboost_LRR", "pos=",pos, "hard=",hard, ".pdf"), width = 8, height = 7)

    dt_tmp<-dt[!(dt$lrnr %in% c("glm", "earth", "gam3", "gam4", "gam5")),]
    dt_tmp <- dt_tmp[grep("rf", dt$lrnr), ]
     max_depth <- stringr::str_match(dt_tmp$lrnr, "([0-9]+)_xg$")[,2]
    max_depth[is.na(max_depth)] <- "cv"
    dt_tmp$lrnr <- paste0("Random forest (", "max_depth=", max_depth,")")
    dt_tmp <- dt_tmp[max_depth %in% c("5", "7", "9", "cv"),]

    plt <- ggplot(dt_tmp, aes(x = n, y = risks_best, group = type, color = type, linetype = type)) + geom_line() +
      facet_wrap(~lrnr, scales = "free") + theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) + ylab("MSE") + scale_y_log10(limits = range(dt_tmp$risks_best))  +  scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
    plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw()+ labs(color = "Method", group = "Method", linetype = "Method")

    ggsave(paste0("mainSimResults/performancePlot_ranger_LRR", "pos=",pos, "hard=",hard, ".pdf"), width = 8, height = 7)



    dt_rf <- dt_tmp

    dt <- rbindlist(sims_list)
    dt[(dt$type == "sieve"),"type"] <- "EP-learner (*)"
    dt[(dt$type == "ipw"),"type"] <- "IPW-learner"
    dt[(dt$type == "subst"),"type"] <- "T-learner"


    dt_tmp<-dt
    dt_tmp <- dt_tmp[ grep("xgboost", dt$lrnr), ]
    max_depth <- stringr::str_match(dt_tmp$lrnr, "([0-9]+)_0")[,2]
    max_depth[is.na(max_depth)] <- "cv"
    dt_tmp$lrnr <- paste0("xgboost (", "max_depth=", max_depth,")")
    dt_tmp <- dt_tmp[max_depth %in% c("1", "5", "cv"),]

    dt_xg <- dt_tmp

    dt <- rbind( dt_xg,dt_rf)
    print(head(dt))
    for(lrnr in unique(dt$lrnr)) {
      print(lrnr)
      dt <- as.data.frame(dt)
      dt <- dt[dt$type != "Oracle DR-Learner",]
      dt_tmp <- dt
      plt <- ggplot(dt[dt$lrnr %in% lrnr,], aes(x = n, y = risks_best, group = type, color = type, linetype = type)) + geom_line(size = 0.75)  + theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) + ylab("MSE") + scale_y_log10(limits = c(min(1e-1, min(dt_tmp$risks_best)), max(dt_tmp$risks_best)))  +  scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000)) +
        facet_wrap(~lrnr, scales = "free")
      plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw() + labs(color = "Method", group = "Method", linetype = "Method")
      plt <- plt +  theme_bw() + theme(axis.text=element_text(size=14),

                                       strip.text.x = element_text(size = 16),
                                       legend.text=element_text(size=12),
                                       legend.title=element_text(size=12),
                                       axis.title=element_text(size=12,face="bold"))
      plt <- plt + theme(legend.justification = c(0.6, 0), legend.position = c(0.75, 0.6))
      #plt <- plt +  scale_colour_manual(labels = c("Causal-Forest", "DR-Learner", "EP-Learner (*)", "T-Learner (cv)" ), values =   c("#619CFF", "#00BA38", "#F8766D", "#E76BF3"))
      #plt <- plt + scale_linetype_manual(labels = c("Causal-Forest", "DR-Learner", "EP-Learner (*)", "T-Learner (cv)" ), values = c("longdash" ,"dashed" , "solid", "dotted"))
      labels <- c( "IPW-learner", "EP-learner (*)", "T-learner" )
      colors <- c("#619CFF", "#00BA38", "#F8766D", "#E76BF3")[-1]
      linetypes <- c("longdash" ,"dashed" , "solid", "dotted")[-1]
      names(colors) <- labels
      names(linetypes) <- labels
      plt <- plt +  scale_colour_manual( values =   colors)
      plt <- plt + scale_linetype_manual ( values = linetypes)
      plt <- plt +
        theme(legend.key.height= unit(0.5, 'cm'),
              legend.key.width= unit(1, 'cm'))  + theme(legend.position = "none")
      ggsave(paste0("mainSimResults/plots/performancePlot_LRR_tree_", "pos=",pos, "hard=",hard, "_", lrnr,".pdf"), width = 4, height = 4)
    }

})
  }
}