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6_modeling3_gamboost.R
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6_modeling3_gamboost.R
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rm(list = ls())
# libraries
pacman::p_load(mboost, tidyverse, lubridate, caret, corrplot, earth, ranger,Metrics, ggplot2,zoo)
outputs <- c("total_count", "Ischemic_count","Bleeding_count")
#read config
if(file.exists("config.yml")){
conf <- config::get(file = "config.yml")
}else{
conf <- config::get(file = "config_default.yml")
}
site.name <- conf$site
### ---------------------------------------------------------------------------
### --------------------------- Pre-processing --------------------------------
### ---------------------------------------------------------------------------
###looped three times for fitting models for three outputs
for(output_counter in outputs){
print(paste("fitting models for", output_counter))
daily <- read.csv(file = file.path(getwd(),"data/daily_level.csv"))
# Outcome based on output_counter
outcome <- daily[[output_counter]]
daily <- daily %>% select(!contains("count"))
daily <- na.locf(daily,na.rm = FALSE)
daily <- na.locf(daily,fromLast = TRUE)
# create daily variables
year <- year(daily$admission_date)
date <- daily$admission_date
daily$admission_date <- NULL
daily <- daily %>%
mutate_at(vars(-day_of_month, -day_of_year,-month,-wday,-year,-week_num,
-mean_prior_week_total,-median_prior_week_total,
-mean_prior_week_ischemic,-median_prior_week_ischemic,
-mean_prior_week_bleeding,-median_prior_week_bleeding
), ~ (scale(.) %>% as.vector))
daily <- subset(daily,select = -c(day_of_month, day_of_year,month,year,week_num))
# create formula
frm <- paste((colnames(daily)[!grepl("count", colnames(daily)) & !grepl("prior_week", colnames(daily))]), collapse = " + ")
if(output_counter == 'total_count'){
frm <- paste0(gsub("\\+ week_prior", "", frm), " + mean_prior_week_total + median_prior_week_total")
}else if(output_counter == 'Ischemic_count') {
frm <- paste0(gsub("\\+ week_prior", "", frm), " + mean_prior_week_ischemic + median_prior_week_ischemic")
}else if(output_counter == 'Bleeding_count') {
frm <- paste0(gsub("\\+ week_prior", "", frm), " + mean_prior_week_bleeding + median_prior_week_bleeding")
}
daily$outcome <- outcome
### ---------------------------------------------------------------------------
### ------------------------------ Models -------------------------------------
### ---------------------------------------------------------------------------
set.seed(32)
check_models <- FALSE
res_list <- list()
## from the Metrics package
metric_funs <- list(
mse,
function(a,p) mape(a+1,p+1), ## Whethere its okay to add 1. Check the usual practice
cor
)
names(metric_funs) <- c("MSE", "MAPE", "COR")
eval_fun <- function(a_tr,a_te,p_tr,p_te,name){
measures_tr <- sapply(metric_funs, function(metr) metr(a_tr, p_tr))
names(measures_tr) <- paste0(names(metric_funs), "_train")
measures_te <- sapply(metric_funs, function(metr) metr(a_te, p_te))
names(measures_te) <- paste0(names(metric_funs), "_test")
data.frame(t(c(measures_tr,measures_te)), model = name)
}
test_index <- which(year(date) == max(year(date)))
train <- daily[-test_index,]
test <- daily[test_index,]
tr_year <- year[-test_index]
#val_folds <- sapply(unique(tr_year), function(y) tr_year == y)
#fixed window false
val_folds<- matrix(nrow =nrow(train) ,ncol = length(unique(tr_year)) - 1)
unique_tr_year <- unique(tr_year)
for(i in 1:(length(unique_tr_year)-1)){
val_folds[which(tr_year <= unique_tr_year[i]),i]<- FALSE
val_folds[which(tr_year >= unique_tr_year[i+1]),i]<- TRUE
}
### ---------------------------------------------------------------------------
### gamboost
print(paste("Fitting Gamboost for",output_counter))
unique_values <- daily %>% summarise_all(n_distinct) %>% t() %>% as.data.frame()
colnames(unique_values) <- "count"
bbs_vars <- rownames(unique_values)[(which(unique_values$count > 24))]
bols_vars <- rownames(unique_values)[(which(unique_values$count <= 24))]
fts <- trimws(strsplit(frm, "\\+")[[1]])
frm_mboost <- paste(c(paste0("bbs(",fts[fts %in% bbs_vars],")"),
paste0("bols(",fts[fts %in% bols_vars],")")
),
collapse = " + ")
try({
# NegBinomial model
mod <- gamboost(formula = as.formula(paste0("outcome ~ ", frm_mboost)),
data = train, family = NBinomial(),
control = boost_control(mstop = 1000L,nu = 0.01))
cvr <- cvrisk(mod, folds = val_folds)
mod[mstop(cvr)]
if(check_models){
plot(cvr)
table(selected(mod))
plot(mod)
plot(train$outcome, predict(mod, type = "response")[,1], col=rgb(0,0,0,0.4))
abline(0, 1, col="red")
}
pr_tr <- predict(mod, type = "response")[,1]
pr_te <- predict(mod, newdata = test, type = "response")[,1]
res_gamboostNB <- eval_fun(train$outcome, test$outcome,
pr_tr, pr_te, name = "gamboostNB")
#save model
saveRDS(object = mod,file = paste("./results/gamboostNB_",output_counter,site.name,".rda",sep = ""))
rm(mod, cvr, pr_tr, pr_te)
}, silent=TRUE)
print(paste("Fitting Gamboost Poisson for",output_counter))
try({
# Poisson model
mod <- gamboost(formula = as.formula(paste0("outcome ~ ", frm_mboost)),
data = train, family = Poisson(), control = boost_control(mstop = 1000L,
nu = 0.01))
cvr <- cvrisk(mod, folds = val_folds)
mod[mstop(cvr)]
if(check_models){
plot(cvr)
table(selected(mod))
plot(mod)
plot(train$outcome, predict(mod, type = "response")[,1], col=rgb(0,0,0,0.4))
abline(0, 1, col="red")
}
pr_tr <- predict(mod, type = "response")[,1]
pr_te <- predict(mod, newdata = test, type = "response")[,1]
res_gamboostPO <- eval_fun(train$outcome, test$outcome,
pr_tr, pr_te, name = "gamboostPO")
#save model
saveRDS(object = mod,file = paste("./results/gamboostPO",output_counter,"_",site.name,".rda",sep = ""))
rm(mod, cvr, pr_tr, pr_te)
}, silent=TRUE)
### ---------------------------------------------------------------------------
### MARS
print(paste("Fitting MARS earth for",output_counter))
mod <- earth(as.formula(paste0("outcome ~ ", frm)), data = train,
degree = 1)
if(check_models){
summary(mod)
# plot non-linear features
plotmo(mod)
plot(train$outcome, predict(mod, type = "response")[,1], col=rgb(0,0,0,0.4))
abline(0, 1, col="red")
}
pr_tr <- predict(mod, type = "response")[,1]
pr_te <- predict(mod, newdata = test, type = "response")[,1]
res_earth1 <- eval_fun(train$outcome, test$outcome,
pr_tr, pr_te, name = "earth1")
#save model
saveRDS(object = mod,file = paste("./results/earth1",output_counter,"_",site.name,".rda",sep = ""))
rm(mod, pr_tr, pr_te)
### ---------------------------------------------------------------------------
### random forest
print(paste("Fitting Random forest for",output_counter))
mod <- ranger(as.formula(paste0("outcome ~ ", frm)), data = train)
if(check_models){
mod
plot(data$outcome, predict(mod, data = train, type = "response")$predictions,
col=rgb(0,0,0,0.4))
abline(0, 1, col="red")
}
pr_tr <- predict(mod, data = train, type = "response")$predictions
pr_te <- predict(mod, data = test, type = "response")$predictions
res_rf <- eval_fun(train$outcome, test$outcome,
pr_tr, pr_te, name = "rf")
#save model
saveRDS(object = mod,file = paste("./results/rf",output_counter,"_",site.name,".rda",sep = ""))
rm(mod, pr_tr, pr_te)
### ---------------------------------------------------------------------------
res_list<- rbind(res_gamboostNB, res_gamboostPO,res_earth1,res_rf)
res_long <- res_list %>% pivot_longer(MSE_train:COR_test) %>%
mutate(data = gsub(".*_(train|test)", "\\1", name),
measure = gsub("(.*)_(train|test)", "\\1", name))
#save residuals
saveRDS(object = res_long,file = paste("./results",output_counter,"_",site.name,".rda",sep = ""))
try({
ggplot(res_long, aes(x = model, y = value, colour = model)) +
geom_point() +
facet_grid(measure ~ data, scales = "free") +
theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggsave(filename = paste("./results/predictions",output_counter,"_",site.name,".pdf",sep = ""), width = 5, height = 5)
}, silent=TRUE)
rm(res_long, res_gamboostNB, res_gamboostPO, res_earth1, res_rf, res_list)
}
print("Modeling gamboost is done")
print("Zip the files in the reults folder to be delivered")
## zip all the files in the results folder to zip : to upload
daily <- read.csv(file = file.path(getwd(),"data/daily_level.csv"))
files2zip <- dir(file.path(getwd(),"results"), full.names = TRUE)
zip_file_name <- paste('westorm-step2-results-', conf$site, "-", Sys.Date(), "-coverage-", min(year), "-", max(year), "-totalcases-", sum(daily$total_count), sep = "")
zip(zipfile = file.path(getwd(),'results', zip_file_name), files = files2zip, extras = '-j')
print(paste("please upload the zip file (/results) :", zip_file_name))
print("Execution is done")