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I wish to run for loop in parallel process. The result I have with the for loop R code is good to my taste but will be applying it to a very huge data thus, the timing of the execution is slow.
library(forecast)
library(dplyr)
arima_order_results = data.frame()
seed_out2 <- c(1, 16, 170, 178, 411, 630, 661, 1242, 1625, 1901, 1926, 1927, 1928, 2170, 2779, 3687, 4139, 4583, 4825, 4828, 4829, 4827, 5103, 5211, 5509, 5561, 5569, 5679, 6344, 6490, 6943, 6944, 6945, 6946, 6948, 6950, 6951, 6952)
for (my_seed in seed_out2){
set.seed(my_seed)
ar1 <- arima.sim(n = 100, model=list(ar = 0.8, order = c(1, 0, 0)), sd = 1)
ar2 <- auto.arima(ar1, ic = "aicc")
arr <- as.data.frame(t(ar2$coef))
if(substr(as.character(arr[1]), 1, 5) == "0.800") {
arr <- cbind(data.frame(seed=my_seed),arr)
print(arr)
arima_order_results = bind_rows(arima_order_results,arr)
# write.csv(my_seed, paste0(arr, ".csv"), row.names = FALSE)
} #else print("NOT AVAILABLE")
}
The result
# seed ar1
#1 170 0.8006368
# seed ar1
#1 411 0.8004152
# seed ar1
#1 630 0.8008459
# seed ar1
#1 661 0.8001553
# seed ar1 intercept
#1 1242 0.8000623 0.8474553
# seed ar1
#1 1625 0.8004982
# seed ar1
#1 1901 0.8007815
# seed ar1
#1 1927 0.8004587
# seed ar1
#1 2170 0.8003091
# seed ar1
#1 2779 0.8008643
#:
#:
#:
#seed ar1
#1 5679 0.800689
# seed ar1 intercept
#1 6344 0.80004 0.9800426
# seed ar1
#1 6490 0.8004093
# seed ar1
#1 6948 0.8006992
What I want
I will want a parallel process that will use up my four processors at the same time so that the job execution will be fast when I apply it to huge data` while I have the same result.
See what I tried
library(parallel)
library(foreach)
library(forecast)
library(dplyr)
library(doSNOW)
cl <- parallel::makeCluster(detectCores(), type = "SOCK")
doSNOW::registerDoSNOW(cl)
arima_order_results = data.frame()
seed_out2 <- c(1, 16, 170, 178, 411, 630, 661, 1242, 1625, 1901, 1926, 1927, 1928, 2170, 2779, 3687, 4139, 4583, 4825, 4828, 4829, 4827, 5103, 5211, 5509, 5561, 5569, 5679, 6344, 6490, 6943, 6944, 6945, 6946, 6948, 6950, 6951, 6952)
lst_out <- foreach::foreach(my_seed = seq_along(seed_out2), .packages = c("dplyr", "forecast") ) %dopar% {
set.seed(my_seed)
ar1 <- arima.sim(n = 100, model=list(ar = 0.8, order = c(1, 0, 0)), sd = 1)
ar2 <- auto.arima(ar1, ic = "aicc")
arr <- as.data.frame(t(ar2$coef))
if(substr(as.character(arr[1]), 1, 5) == "0.800") {
arr <- cbind(data.frame(seed=my_seed),arr)
print(arr)
arima_order_results = bind_rows(arima_order_results,arr)
# write.csv(my_seed, paste0(arr, ".csv"), row.names = FALSE)
}
}
See my trial result
#>lst_out
#[[1]]
#NULL
#[[2]]
#NULL
#[[3]]
#NULL
#[[4]]
#NULL
#:
#:
#:
#[[36]]
#NULL
#[[37]]
#NULL
#[[38]]
#NULL
I am operating on windows.
Edith
I want @jay.sf answer modified in such a way that it will be contain in a function like the function I am providing below.
FUN1 <- function(n, ar, sd, arr, R, FUN2){
FUN2 <- function(i, n, ar, sd, arr) {
set.seed(i)
ar1 <- arima.sim(n=n, model=list(ar=ar, order=c(1, 0, 0)), sd=sd)
ar2 <- auto.arima(ar1, ic="aicc")
(cf <- ar2$coef)
if (length(cf) == 0) {
rep(NA, 2)
}
else if (all(grepl(c("ar1|intercept"), names(cf))) & ## using `grepl`
substr(cf["ar1"], 1, 5) %in% "arr") {
c(cf, seed=i)
}
else {
rep(NA, 2)
}
}
seedv <- 1:R
library(parallel)
cl <- makeCluster(detectCores() - 1)
clusterExport(cl, c("FUN2"), envir=environment())
clusterEvalQ(cl, suppressPackageStartupMessages(library(forecast)))
res <- parLapply(cl, seedv, "FUN2")
res1 <- res[!sapply(res, anyNA)] ## filter out NAs
stopCluster(cl)
}
FUN1(n = 10, ar = 0.8, sd = 1, arr = 0.800, R = 1000, FUN2 = FUN2)
980
Here a similar approach to the answer I gave you to one of your previous related questions.
FUN <- function(i) {
set.seed(i)
ar1 <- arima.sim(n=100, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
ar2 <- auto.arima(ar1, ic="aicc")
cf <- ar2$coef
## case handling
if (length(cf) == 0) rep(NA, 2) ## sometimes result is `character(0)` -> NA
else if (substr(cf[1], 1, 5) %in% "0.800") c(cf, i) ## hit, that's what we want
else rep(NA, 2) ## all other cases -> NA
}
R <- 1e3 ## this would be your 1e5
seedv <- 1:R ## or use custom seed vector
library(parallel)
cl <- makeCluster(detectCores() - 1) ## for all cores remove `- 1`
clusterExport(cl, c("FUN"), envir=environment())
clusterEvalQ(cl, suppressPackageStartupMessages(library(forecast)))
res <- `colnames<-`(t(parSapply(cl, seedv, "FUN")), c("cf", "seed"))
stopCluster(cl)
In the result we want to filter out all the rows with NA.
head(res[!is.na(res[,1]), ])
# cf seed
# [1,] 0.8006368 170
# [2,] 0.8004152 411
# [3,] 0.8008459 630
# [4,] 0.8001553 661
To include auto.arima results just containing combinations of "ar1" and "intercept" we better use parLapply:
FUN <- function(i) {
set.seed(i)
ar1 <- arima.sim(n=50, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
ar2 <- auto.arima(ar1, ic="aicc")
(cf <- ar2$coef)
if (length(cf) == 0) {
rep(NA, 2)
}
else if (all(grepl(c("ar1|intercept"), names(cf))) & ## using `grepl`
substr(cf["ar1"], 1, 5) %in% "0.800") {
c(cf, seed=i)
}
else {
rep(NA, 2)
}
}
R <- 1e4
seedv <- 1:R
library(parallel)
cl <- makeCluster(detectCores() - 1)
clusterExport(cl, c("FUN"), envir=environment())
clusterEvalQ(cl, suppressPackageStartupMessages(library(forecast)))
res <- parLapply(cl, seedv, "FUN")
res1 <- res[!sapply(res, anyNA)] ## filter out NAs
stopCluster(cl)
This gives a list of data frames with unequal column lengths, that we may merge with Reduce.
res2 <- Reduce(function(...) merge(..., all=T), lapply(res1, function(x) as.data.frame(t(x))))
res2[order(res2$seed), c("ar1", "intercept", "seed")] ## some ordering
# ar1 intercept seed
# 1 0.8000531 1.335388 290
# 3 0.8002499 NA 2154
# 10 0.8005477 NA 2888
# 11 0.8006736 NA 3203
# 15 0.8009363 NA 4415
# 14 0.8008462 NA 4572
# 4 0.8003495 NA 4726
# 9 0.8005087 NA 6241
# 2 0.8001865 NA 6417
# 13 0.8008060 -1.700587 6845
# 6 0.8003977 NA 7187
# 8 0.8004316 NA 8981
# 7 0.8004268 NA 9368
# 12 0.8007281 NA 9697
# 5 0.8003903 NA 9793
Here is a function that only requires the user to specify R - the number of iterations. It internally uses doParallel::registerDoParallel to define an implicit cluster which uses the usual detectCores() - 1 by default but may also be specified by the user. The clusters will be stopped automatically. Furthermore, a foreach loop is applied.
library(forecast)
library(doParallel)
arimaze <- function(R, ncores=detectCores() - 1) {
registerDoParallel(ncores)
seedv <- 1:R
FUN <- function(i) {
set.seed(i)
ar1 <- arima.sim(n=50, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
ar2 <- auto.arima(ar1, ic="aicc")
cf <- ar2$coef
if (length(cf) == 0 | !(all(grepl(c("ar1|intercept"), names(cf))) &
substr(cf["ar1"], 1, 5) %in% "0.800")) {
return(rep(NA, 3))
} else {
cf <- `length<-`(cf, 2)
return(c(cf, seed=i))
}
}
message('processing...')
res <-
foreach(i=seedv, .combine=rbind.data.frame, .packages='forecast') %dopar%
FUN(i)
message(' done!\n')
res <- `rownames<-`(res[rowSums(is.na(res)) == 0, ], NULL)
stopImplicitCluster()
return(setNames(res, c('ar', 'intercept', 'seed')))
}
r <- arimaze(1.5e4)
# processing... done!
r
# ar intercept seed
# 1 0.8000531 1.335388 290
# 2 0.8008060 -1.700587 6845
# 3 0.8003690 -1.443856 12137
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