FinalSimulationCode/performancePlot_xgboost_highdim.R
In Larsvanderlaan/npcausalML: Nonparametric estimation of the relative risk function
library(ggplot2)
library(data.table)
ns <- c( 500, 1000, 2500)
ns <- sort(ns)
hard_list <- c(T,F)
pos_list <- c(T,F)
use_oracle_sieve <- F
for(pos in pos_list){
for(hard in hard_list) {
try({
sims_list <- lapply(ns, function(n) {
try({load(paste0("mainSimResults2/mainSimResults2/simsCATE", hard, pos, "n", n, "_xgboost_highDim"))
simresults <- get(paste0("simresults"))
simresults <- simresults[sapply(simresults, is.list)]
onestepbenchoracle <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "CATEonestepbenchoracle")))
onestepbench <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "CATEonestepbench")))
causalforestrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_cf")))
substrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_subst")))
cvsubstrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_subst_cv")))
lrnr_names <- names(simresults[[1]]$CATEonestepbench) #simresults[[1]]$sieve[[1]]
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-Plugin"
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$lrnr_full)] <- 0
tmp <- data.table(risks_oracle = onestepbench, lrnr_full = names(onestepbench), degree = "One-step")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = onestepbenchoracle, lrnr_full = names(onestepbench), degree = "Oracle one-step")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = cvsubstrisks, lrnr_full =names(onestepbench), degree = "Substitution-CV")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = causalforestrisks, lrnr_full = names(onestepbench), lrnr = "Causal-Forest", degree = "Causal-Forest")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = substrisks, lrnr_full = names(onestepbench), degree = "Substitution")
dt <- rbind(dt, tmp, fill = T)
dt$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$degree))] <- "Sieve-Plugin"
dt$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)
})
})
sims_list <- sims_list[sapply(sims_list, is.data.frame)]
dt <- rbindlist(sims_list)
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", "2", "3", "5", "cv"),]
dt_tmp <- rbind(dt_tmp, dt[grep("Causal-Forest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (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$risks_best)) + scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw() + labs(color = "Method", group = "Method", linetype = "Method")
plt <- plt + theme_bw() + theme(axis.text=element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plt <- plt + theme(legend.justification = c(0.9, 0), legend.position = c(0.9, 0))
ggsave(paste0("mainSimResults/performancePlot_CATE_highDim_xgboost", "pos=",pos, "hard=",hard, ".pdf"), width = 8, height = 7)
dt_tmp<-dt[!(dt$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("ranger (", "max_depth=", max_depth,")")
dt_tmp <- dt_tmp[max_depth %in% c("3", "5", "7", "9", "cv"),]
dt_tmp$lrnr <- factor(dt_tmp$lrnr, levels = paste0("ranger (", "max_depth=", c("3", "5", "7", "9", "cv"),")"))
dt_tmp <- rbind(dt_tmp, dt[grep("causalforest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (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$risks_best)) + scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw()+ labs(color = "Method", group = "Method", linetype = "Method")
plt <- plt + theme_bw() + theme(axis.text=element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plt <- plt + theme(legend.justification = c(0.9, 0), legend.position = c(0.9, 0))
ggsave(paste0("mainSimResults/performancePlot_CATE_highDim_ranger", "pos=",pos, "hard=",hard, ".pdf"), width = 8, height = 7)
dt_tmp<-dt[!(dt$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("ranger (", "max_depth=", max_depth,")")
dt_tmp <- dt_tmp[max_depth %in% c( "3", "7" ,"cv"),]
dt_tmp$lrnr <- factor(dt_tmp$lrnr, levels = paste0("ranger (", "max_depth=", c("3", "5", "7", "9", "cv"),")"))
dt_tmp <- rbind(dt_tmp, dt[grep("causalforest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (CV)"
dt_rf <- dt_tmp
dt <- rbindlist(sims_list)
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_tmp <- rbind(dt_tmp, dt[grep("Causal-Forest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (CV)"
dt_xg <- dt_tmp
dt <- rbind( dt_xg,dt_rf)
plt <- ggplot(dt, 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$risks_best)) + scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw()+ labs(color = "Method", group = "Method", linetype = "Method")
plt <- plt + theme_bw() + theme(axis.text=element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plt <- plt + theme(legend.justification = c(0.9, 0), legend.position = c(0.95, 0.2))
ggsave(paste0("mainSimResults/performancePlot_CATE_highDim_tree", "pos=",pos, "hard=",hard, "mainpaper.pdf"), width = 8, height = 7)
for(lrnr in unique(dt$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 = 20),
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("Causal-Forest", "DR-Learner", "EP-Learner (*)", "T-Learner (CV)" )
colors <- c("#619CFF", "#00BA38", "#F8766D", "#E76BF3")
linetypes <- c("longdash" ,"dashed" , "solid", "dotted")
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_CATE_highDim_tree", "pos=",pos, "hard=",hard, "_", lrnr,".pdf"), width = 4, height = 4)
}
})
}
}
Larsvanderlaan/npcausalML documentation built on July 30, 2023, 4:32 p.m.
R Package Documentation
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library(ggplot2)
library(data.table)
ns <- c( 500, 1000, 2500)
ns <- sort(ns)
hard_list <- c(T,F)
pos_list <- c(T,F)
use_oracle_sieve <- F
for(pos in pos_list){
for(hard in hard_list) {
try({
sims_list <- lapply(ns, function(n) {
try({load(paste0("mainSimResults2/mainSimResults2/simsCATE", hard, pos, "n", n, "_xgboost_highDim"))
simresults <- get(paste0("simresults"))
simresults <- simresults[sapply(simresults, is.list)]
onestepbenchoracle <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "CATEonestepbenchoracle")))
onestepbench <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "CATEonestepbench")))
causalforestrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_cf")))
substrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_subst")))
cvsubstrisks <- rowMeans(do.call(cbind, lapply(simresults, `[[`, "risk_subst_cv")))
lrnr_names <- names(simresults[[1]]$CATEonestepbench) #simresults[[1]]$sieve[[1]]
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-Plugin"
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$lrnr_full)] <- 0
tmp <- data.table(risks_oracle = onestepbench, lrnr_full = names(onestepbench), degree = "One-step")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = onestepbenchoracle, lrnr_full = names(onestepbench), degree = "Oracle one-step")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = cvsubstrisks, lrnr_full =names(onestepbench), degree = "Substitution-CV")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = causalforestrisks, lrnr_full = names(onestepbench), lrnr = "Causal-Forest", degree = "Causal-Forest")
dt <- rbind(dt, tmp, fill = T)
tmp <- data.table(risks_oracle = substrisks, lrnr_full = names(onestepbench), degree = "Substitution")
dt <- rbind(dt, tmp, fill = T)
dt$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$degree))] <- "Sieve-Plugin"
dt$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)
})
})
sims_list <- sims_list[sapply(sims_list, is.data.frame)]
dt <- rbindlist(sims_list)
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", "2", "3", "5", "cv"),]
dt_tmp <- rbind(dt_tmp, dt[grep("Causal-Forest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (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$risks_best)) + scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw() + labs(color = "Method", group = "Method", linetype = "Method")
plt <- plt + theme_bw() + theme(axis.text=element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plt <- plt + theme(legend.justification = c(0.9, 0), legend.position = c(0.9, 0))
ggsave(paste0("mainSimResults/performancePlot_CATE_highDim_xgboost", "pos=",pos, "hard=",hard, ".pdf"), width = 8, height = 7)
dt_tmp<-dt[!(dt$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("ranger (", "max_depth=", max_depth,")")
dt_tmp <- dt_tmp[max_depth %in% c("3", "5", "7", "9", "cv"),]
dt_tmp$lrnr <- factor(dt_tmp$lrnr, levels = paste0("ranger (", "max_depth=", c("3", "5", "7", "9", "cv"),")"))
dt_tmp <- rbind(dt_tmp, dt[grep("causalforest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (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$risks_best)) + scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw()+ labs(color = "Method", group = "Method", linetype = "Method")
plt <- plt + theme_bw() + theme(axis.text=element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plt <- plt + theme(legend.justification = c(0.9, 0), legend.position = c(0.9, 0))
ggsave(paste0("mainSimResults/performancePlot_CATE_highDim_ranger", "pos=",pos, "hard=",hard, ".pdf"), width = 8, height = 7)
dt_tmp<-dt[!(dt$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("ranger (", "max_depth=", max_depth,")")
dt_tmp <- dt_tmp[max_depth %in% c( "3", "7" ,"cv"),]
dt_tmp$lrnr <- factor(dt_tmp$lrnr, levels = paste0("ranger (", "max_depth=", c("3", "5", "7", "9", "cv"),")"))
dt_tmp <- rbind(dt_tmp, dt[grep("causalforest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (CV)"
dt_rf <- dt_tmp
dt <- rbindlist(sims_list)
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_tmp <- rbind(dt_tmp, dt[grep("Causal-Forest", dt$lrnr),])
dt_tmp <- dt_tmp[!(dt_tmp$type == "Substitution"),]
dt_tmp[(dt_tmp$type == "Sieve-Plugin"),"type"] <- "EP-Learner (*)"
dt_tmp[(dt_tmp$type == "One-step"),"type"] <- "DR-Learner"
dt_tmp[(dt_tmp$type == "Oracle one-step"),"type"] <- "Oracle DR-Learner"
dt_tmp[(dt_tmp$type == "Substitution-CV"),"type"] <- "T-Learner (CV)"
dt_xg <- dt_tmp
dt <- rbind( dt_xg,dt_rf)
plt <- ggplot(dt, 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$risks_best)) + scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000))
plt <- plt + xlab("Sample Size (n)") + ylab("Mean-Squared-Error (MSE)") + theme_bw()+ labs(color = "Method", group = "Method", linetype = "Method")
plt <- plt + theme_bw() + theme(axis.text=element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plt <- plt + theme(legend.justification = c(0.9, 0), legend.position = c(0.95, 0.2))
ggsave(paste0("mainSimResults/performancePlot_CATE_highDim_tree", "pos=",pos, "hard=",hard, "mainpaper.pdf"), width = 8, height = 7)
for(lrnr in unique(dt$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 = 20),
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("Causal-Forest", "DR-Learner", "EP-Learner (*)", "T-Learner (CV)" )
colors <- c("#619CFF", "#00BA38", "#F8766D", "#E76BF3")
linetypes <- c("longdash" ,"dashed" , "solid", "dotted")
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_CATE_highDim_tree", "pos=",pos, "hard=",hard, "_", lrnr,".pdf"), width = 4, height = 4)
}
})
}
}
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