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rf_up_statusDay28_kenya_complete_AL_cv.R
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rf_up_statusDay28_kenya_complete_AL_cv.R
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#load("C:\\plu\\project\\Kenya Study\\Pf1000_USAMRU-K_ACTs_Dataxfer.rdata")
# convert .RData -> .rdb/.rdx
e = local({load("/home/plu/data/kenya/Pf1000_USAMRU-K_ACTs_Dataxfer.rdata"); environment()})
tools:::makeLazyLoadDB(e, "New")
lazyLoad("New")
library(caret)
library(doMC)
# library(gtools)
library(stringr)
library(data.table)
#identify and remove features with near zero variance and zero variance
q <- data.frame(t(wide.df))
h <- nearZeroVar(q, saveMetrics=TRUE)
q <- q[,(colnames(q) %in% rownames(h[h[,"nzv"]==FALSE | h[,"zeroVar"]==TRUE,]))]
q <- q[,(colnames(q) %in% rownames(h[h[,"zeroVar"]==FALSE,]))]
#identify and remove highly correlated features
# rho = cor(q, method="pearson", use="pairwise.complete.obs")
# iiRemove <- findCorrelation(rho, cutoff=0.75) # indices to remove to reduce pair-wise corr
# X <- q[, -iiRemove]
# dim(X)
#corrplot(rho, method = "color")
# if (anyNA(q)){
# #check for features (columns) and samples (rows) where more than 5% of the data is missing
# pMiss <- function(x){sum(is.na(x))/length(x)*100}
# #check samples (rows)
# rmiss <- apply(t(wide.df),1,pMiss)
# hist(rmiss)
# #check features (columns)
# cmiss <- apply(t(wide.df),2,pMiss)
# hist(cmiss)
#
# #immputation/missing value predictation
# registerDoMC(cores = 6)
# preProc_Imput <- preProcess(method="bagImpute", q)
# complete_q <- predict(preProc_Imput, q)
# }
#
# write.csv(complete_q , "/home/plu/imp_kenya_20190315.csv")
myData <- read.csv("/home/plu/data/kenya/imp_kenya_20190315.csv",row.names = 1, header = TRUE)
#myData <- read.csv("C:\\plu\\project\\Kenya Study\\data\\imp_kenya_20190315.csv",row.names = 1, header = TRUE)
if(anyNA(myData)){
myData <- myData[ , colSums(is.na(myData)) == 0]
}
RF <- function(X, Y, repeats){
# auc_obj <- c()
# Sens <- c()
# Spec <- c(0)
# nfold = 5
# ntime = 10
#
# set.seed(112358)
# idx <- createMultiFolds(Y,k = nfold, times = ntime)
# #sapply(idx, length)
#aucroc <- c()
# set.seed(112358)
# results <- foreach(i = 1:(nfold*ntime), .combine = cbind) %dopar% {
#repeats <- 100
set.seed(112358)
control <- trainControl(
method = "repeatedcv",
number = 5,
repeats = repeats,
verboseIter = FALSE,
returnData = FALSE,
allowParallel = TRUE,
sampling = "up",
selectionFunction = "oneSE",
summaryFunction = twoClassSummary,
classProbs = TRUE,
savePredictions = T
)
set.seed(112358)
rfUnbalance <- train(
x = data.frame(X),
y = Y,
method = "rf",
ntree = 1500,
tuneLength = 10,
metric = "ROC",
preProcess = c("center", "scale"),
trControl = control)
#print(rfUnbalance)
#print(rfUnbalance$finalModel)
#imp <- varImp(rfUnbalance)
#confusionMatrix(rfUnbalance$finalModel$predicted,Y, positive = 'Protected')
#return(rfUnbalance$results$ROC[as.numeric(row.names(rfUnbalance$bestTune))])
return(rfUnbalance)
}
permutationTEST <- function(X, Y, r){
ls <- RF(X, Y, 100)
df <- ls$resample
df$Rep <- stringr::str_split_fixed(df$Resample, '\\.', 2)[,2]
varImp <- varImp(ls)$importance
varImp$Ann <- ann.df[row.names(varImp(ls)$importance),"Description"]
write(data.table::setDT(df)[, mean(ROC), by=Rep]$V1, "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806_aucroc", ncolumns = 1)
write.csv(ls$finalModel$confusion, "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806_finalModel.csv", row.names = TRUE, col.names = TRUE)
write.csv(varImp[order(varImp$Overall,decreasing = TRUE),], "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806_varImp.csv", row.names = TRUE, col.names = TRUE)
prediction_data = ls$pred[ls$pred$mtry == ls$finalModel$mtry, ]
write.csv(confusionMatrix(prediction_data$pred, prediction_data$obs)$table, "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806_accuracyCV.csv",row.names = TRUE, col.names = TRUE)
write(confusionMatrix(prediction_data$pred, prediction_data$obs)$overall[2], "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806_Kappa")
e <- mean(df$ROC)
print(paste("e is ",e))
c <- 0
permutes <- c()
holder <- c()
ep <- c()
for(i in 1:r){
permutes <- as.character(sample(Y, replace = FALSE))
holder <- rbind(holder, permutes)
}
for (i in 1:r) {
print(paste(i,"permutation"))
ep[i] <- mean(RF(X, holder[i,], 10)$resample$ROC)
print(ep[i])
if(ep[i] >= e){
c <- c + 1
}
}
#results <- foreach(i = 1:r, .combine = cbind) %dopar% {
# print(i)
# ep <- greedyRLS(X, holder[i,], k)
# print(ep)
# return(ep)
# }
# for(j in 1:r){
# if(results[j] >= e){
# c <- c + 1
# }
#}
p <- c/r
return(list("P"= p, "aucroc" = e, "aucroc_permutation" = ep, "permutes" = holder))
}
# results_combine_trans <- read.csv("C:\\plu\\project\\ML_malaria_vaccine\\MAL027_R\\GSE89292_D59.csv",row.names = T)[-1]
# outcome <- read.csv("C:\\plu\\project\\ML_malaria_vaccine\\MAL027_R\\GSE89292_outcome.csv")[-1]
#results_combine_trans <- read.csv("/home/plu/data/MAL027/GSE89292_D59.csv")[-1]
#outcome <- read.csv("/home/plu/data/MAL027/GSE89292_outcome.csv")[-1]
registerDoMC(cores = 8)
r = 100
# X <- myData[which(!(is.na(pheno.df$ACPR42))),]
# names(X) <- make.names(names(X))
# Y <- pheno.df[which(!(is.na(pheno.df$ACPR42))),"ACPR42"]
# Y <- as.factor(Y)
# levels(Y) <- c("NP", "P")
# output <- permutationTEST(X, Y, r)
X <- myData[which(pheno.df$Study.Arm == "AL" & !(is.na(pheno.df$ACPR28))),]
names(X) <- make.names(names(X))
Y <- pheno.df[which(pheno.df$Study.Arm == "AL" & !(is.na(pheno.df$ACPR28))),"ACPR28"]
Y <- as.factor(Y)
levels(Y) <- c("NP", "P")
output <- permutationTEST(X, Y, r)
lapply(output, print)
write.csv(output$permutes, "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806.csv")
write(output$aucroc_permutation, "/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806_aucrocPerm", ncolumns = 1)
#prediction_data = ls$pred[ls$pred$mtry == ls$finalModel$mtry, ]
#print(confusionMatrix(prediction_data$pred, prediction_data$obs)$table)
save.image("/home/plu/rf_up_statusDay28_kenya_complete_AL_cv_20200806.RData")