问题描述
我有一个 R
代码,当我使用 arima.sim()
函数来模拟 时,它可以帮助我知道什么
它实际上会在 seed
>ARIMA(1, 0, 0)auto.arima()
时模拟 order 1, 0, 0
的 ARIMA
代码>函数用于检查.
I Have an R
code that helps me to know at what seed
when I use arima.sim()
function to simulate ARIMA(1, 0, 0)
it will actually simulate ARIMA
of order 1, 0, 0
when auto.arima()
function is employed for a check.
MWE
library(forecast)
SEED_vector <- 1:10
arima_order_results <- data.frame()
flag <- TRUE
i <- 1
seed_out <- c()
while(flag){
set.seed(SEED_vector[i])
ar1 <- arima.sim(n = 20, model=list(ar=0.8, order = c(1, 0, 0)), sd = 1)
ar2 <- auto.arima(ar1, ic = "aicc")
if(all(arimaorder(ar2)==c(1,0,0))) {
#print(arima_order_results)
print(paste0('arimaorder', SEED_vector[i], ' ' ,
paste(arimaorder(ar2), collapse=" ")))
seed_out <- c(seed_out, SEED_vector[i])
}
arima_order = arimaorder(ar2)
arima_order = t(as.data.frame(arima_order))
arima_order_results = rbind(arima_order_results,arima_order)
i <- i+1
if(i == length(SEED_vector)) {
flag <- FALSE
}
}
我对运行时设置的种子感兴趣
I am interested in what seed will I set such that when I run
set.seed(seed_out)
ar1 <- arima.sim(n = 20, model=list(ar=0.8, order = c(1, 0, 0)), sd = 1)
auto.arima(ar1, ic = "aicc")
它会给我 (1, 0, 0) 的
MWEarimaorder
.在我的 种子是
2and
3`.
it will give me arimaorder
of (1, 0, 0). In my
MWEthe seeds are
2and
3`.
我想要的
我希望在 parallel processing
中使用我的 MWE
,因为我实际上正在运行 1 到 100,000 个种子,这需要 3 个小时.
I want this my MWE
in parallel processing
because I am actually running for seeds of 1 to 100,000 and it is taking 3 hours.
我在 Windows 上运行 R
I am running R
on windows
推荐答案
您可以设置一个 FUN
动作来与 parallel::parSapply
并行化.我相信 print
ing 不会那么容易(类似于进度条之类的东西),所以我把它排除在外.FUN()
将 ar2
的 arima 顺序与种子连接起来,因此 parSapply
的结果将是一个很好的矩阵 res代码>,您可以在此查看 arima 顺序和
seed
之后.
You could set up a FUN
ction to parallelize with parallel::parSapply
. I believe the print
ing wouldn't work so easily (similar to progress bars and such stuff) so I leave it out. FUN()
concatenates the arima order of ar2
with the seed, thus the result of parSapply
will be a nice matrix res
, where you may check arima order and seed
afterwards.
FUN <- function(i) {
set.seed(i)
ar1 <- arima.sim(n=20, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
ar2 <- auto.arima(ar1, ic="aicc")
c(arimaorder(ar2), seed=i)
}
要并行化,请设置一个种子向量,您将在该向量上使用 parSapply
进行循环.FUN"
和 forecast"
包需要导出到集群中.
To parallelize, set up a seed vector over which you'll loop with parSapply
. "FUN"
and the "forecast"
package need to be exported to the clusters.
R <- 1e2 ## this would be your 1e5
seedv <- 1:R
library(parallel)
cl <- makeCluster(detectCores() - 1)
clusterExport(cl, c("FUN"), envir=environment())
clusterEvalQ(cl, suppressPackageStartupMessages(library(forecast)))
res <- parSapply(cl, seedv, "FUN")
stopCluster(cl)
在结果矩阵res
中,
res
# [,1] [,2] [,3] [,4] [,5] [,6]
# p 2 1 1 0 2 ...
# d 0 0 0 1 0 ...
# q 0 0 0 0 0 ...
# seed 1 2 3 4 5 ...
你可以查询哪个seed"
的arima顺序是c(1, 0, 0)
.
you may look-up for which "seed"
the arima order is c(1, 0, 0)
.
res["seed", which(apply(res, 2, function(x) all(x[1:3] == c(1, 0, 0))))]
# [1] 2 3 11 16 17 23 24 25 28 30 33 34 42 43 45 50 51 54 59 60 63 64 66 67
# [25] 71 72 77 79 84 91 96 97
我用我的机器检查了 seedv
长度为 1e3,预计 1e5 的预计长度的执行时间为
I checked with seedv
length 1e3 with my machine and would expect an execution time of <30 min for the projected length of 1e5.
seedv <- 1:1e3
system.time(parSapply(cl, seedv, "FUN"))
# user system elapsed
# 0.00 0.00 17.05
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