I am trying to convert the following code into parallel using foreach and %dopar%.
library(doSNOW)
library(foreach)
cl<- makeCluster(4, type = "SOCK")
registerDoSNOW(cl)
min_subid <- c()
max_subid <- c()
p_typ <- c()
p_nm <- c()
st_tm<-c()
end_tm <- c()
supp <- c()
chart_type <- c()
foreach(j =1:noOfPhases) %dopar%
{
start_time <-phases[j, colnames(phases)=="StartTime"]
end_time <-phases[j, colnames(phases)=="StopTime"]
phase_type <-phases[j, colnames(phases)=="Phase_Type_Id"]
phase_name <-phases[j, colnames(phases)=="Phase_Name"]
suppress <-phases[j, colnames(phases)=="Suppression_Time"]
chart_typ <-phases[j, colnames(phases)=="chartType"]
conft<-(masterData$Time.Subgroup>=start_time & masterData$Time.Subgroup<=end_time)
masterData[which(conft), colnames(masterData)=="Phase_Type"]<-phase_type
masterData[which(conft), colnames(masterData)=="Phase_Name"]<-phase_name
min_subid <- rbind(min_subid, min(which(conft)))
max_subid <- rbind(max_subid, max(which(conft)))
p_typ <- rbind( p_typ, masterData$Phase_Type[min(which(conft))])
p_nm <- rbind( p_nm, masterData$Phase_Name[min(which(conft))])
st_tm <- rbind( st_tm, as.character(start_time))
end_tm <- rbind( end_tm, as.character(end_time))
supp <- rbind(supp,as.character(suppress))
chart_type <- rbind(chart_type,as.character(chart_typ))
phase_info <- data.frame(Subgrp_No_Start=min_subid, Subgrp_No_End=max_subid, Phase_Type=p_typ,
Phase_Name=p_nm, Start_Time=st_tm, Stop_Time=end_tm,
Suppression_Time=supp,ChartType=chart_type)
}
phase_output<-merge(phase_info, phases, by.x=c("Start_Time",
"Stop_Time","ChartType"), by.y=c("StartTime", "StopTime","chartType"))
The above code executes successfully when %do% is included instead of %dopar%. can anyone help me in understanding why I get the following error when it runs parallel (%dopar%) and runs successfully on sequential (%do%)
Error in merge(phase_info, phases, by.x = c("Start_Time", "Stop_Time", :
object 'phase_info' not found
The solution is really simple, but I start off with an explanation of what is happening when you execute the code to explain the error.
What happens in your foreach block is that the one data frame (phase_info) is created for each value of j and they are returned together in a list. However, since your assignment phase_info <- data.frame(...) is located inside the foreach rather than outside, the list is not stored anywhere and gets discarded. The cause for confusion is that when using %do% you create all the data frames sequentially on the master node and when using %dopar% the frames are being created in parallel on the worker nodes. The following merge command is executed on the master node causing an error if you used %dopar% since phase_info does not exist in its workspace. Also note that when using %do% like above, each iterations of foreach overwrites the result of the previous ones (i.e. you get only the result of the last iteration).
This minor change fixes it:
phase_info <- foreach(...) %dopar% {
...
data.frame(Subgrp_No_Start=min_subid, Subgrp_No_End=max_subid, Phase_Type=p_typ,
Phase_Name=p_nm, Start_Time=st_tm, Stop_Time=end_tm,
Suppression_Time=supp,ChartType=chart_type)
# No need to give it a name as it will be returned and the name forgotten
}
phase_output <- merge(phase_info, ...)
As I mentioned above, phase_info will now be a list where each element is a data frame. I am just guessing now but you probably want to execute the merge elementwise then, like this:
phase_output <- lapply(phase_info, merge, phases, by.x=c("Start_Time",
"Stop_Time","ChartType"), by.y=c("StartTime", "StopTime","chartType"))
Related
I have found a feature/bug in the foreach package, which I do not understand. Perhaps someone can explain me this behaviour:
I created a for-loop with the foreach package (I use them together with mutlicore calculations, but here just in a sequentiell example, the bug appears in both variants). This loop runs r times. In every run a list with c entries is returned. So I expect a list with r entries, and every entry consists of c lists.
My code was the following one:
library(foreach)
clusters <- 10
runs <- 100
temp <- foreach(r = 1:runs,
.combine = 'list',
.multicombine = TRUE) %do% {
signal_all <- lapply(1:clusters, function(x){
return(1)
})
return(signal_all)
} ## end do
With this code, all works as expected, see the following picture:
But when increasing runs <- 101, the output temp is this:
The expected list structure is destroyed. But when commenting out the line .combine = 'list' all works as expected.
library(foreach)
clusters <- 10
runs <- 100
temp <- foreach(r = 1:runs,
.multicombine = TRUE) %do% {
signal_all <- lapply(1:clusters, function(x){
return(1)
})
return(signal_all)
} ## end do
Can someone explain this behaviour?
Thanks for any help!
Meanwhile I have found a solution.
The foreach function knows that some comine-functions (e.g. c or cbind) take many arguments, and will call them with up to 100 arguments (by default) in order to improve performance. With the argument .maxcombine you can set them manually.
library(foreach)
clusters <- 10
runs <- 101
temp <- foreach(r = 1:runs,
.combine = 'list',
.maxcombine = runs,
.multicombine = T) %do% {
signal_all <- lapply(1:clusters, function(x){
return(1)
})
return(signal_all)
} ## end do
the following is a parallel loop I am trying to run in R:
cl <- makeCluster(30,type="SOCK")
registerDoSNOW(cl)
results <- foreach (i = 1:30, .combine='bindlist', .multicombine=TRUE) %dopar% {
test <- i
test <- as.list(test)
list(test)
}
stopCluster(cl)
The output of my code is always a list and I want to combine the list into one large list. Thus I wrote the following .combine function:
bindlist <- function(x,y,...){
append(list(x),list(y),list(...))
}
As I am doing multiple runs and the number of variables change I tried to use .... However it does not work. How can I rewrite the .combine function so it can work with changing numbers of variables?
Have you considered using 'c'
results <- foreach (i = 1:4, .combine='c', .multicombine=TRUE) %dopar% {
test <- i
test <- as.list(test)
list(test)
}
If this adds an additional unwanted 'level' to your results, you could use 'unlist' to remove that level.
unlist(results, recursive = FALSE)
I'd like to know whether the cpv function within the trotter package works with %dopar%? I'm getting the following error:
task 1 failed - "object of type 'S4' is not subsettable"
Here's a small example:
library(doParallel)
library(trotter)
registerDoParallel(cores = 2)
x <- letters
combos <- cpv(2, 1:4)
print(combos)
num_combos <- length(combos)
results_list <- foreach(combo_num=1:num_combos) %dopar% { # many iterations
y <- x[combos[combo_num]]
# time consuming stuff follows that involves using y
}
Replacing %dopar% with %do% (or simply using a for loop) and it works fine.
Depending on the cluster type one needs to explicitly specify the used packages via the .packages argument. The following should work:
library(doParallel)
library(trotter)
cl <- makePSOCKcluster(2)
registerDoParallel(cl=cl)
x <- letters
combos <- cpv(2, 1:4)
num_combos <- length(combos)
rl <- foreach(combo_num=1:num_combos, .packages="trotter") %dopar% {
x[combos[combo_num]]
}
I am using
library(foreach)
library(doSNOW)
And I have a function mystoploss(data,n=14)
I then call it like that (I want to loop over n=14 for now):
registerDoSNOW(makeCluster(4, type = "SOCK"))
foreach(i = 14) %dopar% {assign(paste("Performance",i,sep=""),
mystoploss(data=mydata,n=i))}
I then try to find Performance14 from above, but it is not assigned.
Is there some way to assign so the output will be in Performance14?
And if I use
foreach(i = 14) %dopar% {assign(paste("Performance",i,sep=""),
mystoploss(data=mydata,n=i),envir = .GlobalEnv)}
I get error :
Error in e$fun(obj, substitute(ex), parent.frame(), e$data) :
worker initialization failed: Error in as.name
Best Regards
This is because the assign operations are happening in the worker processes. The vaues of the variables are being sent back (see your R session console) but not with the names you assigned. You need to capture these values and assign them names again. See this related question.
The following is an alternative that may be of help: asign the output of foreach to an intermediate variable and assign it to your desired variables in the current 'master process' environment.
PerformanceAll <- foreach(i = 1:14,.combine="c") %dopar% { mystoploss(data=mydata,n=i) } #pick .combine appropriately
for(i in 1:14){ assign(paste("Performance",i,sep=""), PerformanceAll[i]) }
Here is the full example I tried:
library(foreach)
library(doSNOW)
mystoploss <- function(data=1,n=1){
return(runif(data)) #some operation, returns a scalar
}
mydata <- 1
registerDoSNOW(makeCluster(4, type = "SOCK"))
PerformanceAll <- foreach(i = 1:14,.combine="c") %dopar% { mystoploss(data=mydata,n=i) }#pick .combine appropriately
for(i in 1:14){ assign(paste("Performance",i,sep=""), PerformanceAll[i]) }
Edit: If the output of mystoploss is a list, then do the following changes:
mystoploss <- function(data=1,n=1){#Example
return(list(a=runif(data),b=1)) #some operation, return a list
}
PerformanceAll <- foreach(i = 1:14) %dopar% { mystoploss(data=mydata,n=i) }#remove .combine
for(i in 1:14){ assign(paste("Performance",i,sep=""), PerformanceAll[[i]]) } #double brackets
I would like to know if/how it would be possible to return multiple outputs as part of foreach dopar loop.
Let's take a very simplistic example. Let's suppose I would like to do 2 operations as part of the foreach loop, and would like to return or save the results of both operations for each value of i.
For only one output to return, it would be as simple as:
library(foreach)
library(doParallel)
cl <- makeCluster(3)
registerDoParallel(cl)
oper1 <- foreach(i=1:100000) %dopar% {
i+2
}
oper1 would be a list with 100000 elements, each element is the result of the operation i+2 for each value of i.
Suppose now I would like to return or save the results of two different operations separately, e.g. i+2 and i+3. I tried the following:
oper1 = list()
oper2 <- foreach(i=1:100000) %dopar% {
oper1[[i]] = i+2
return(i+3)
}
hoping that the results of i+2 will be saved in the list oper1, and that the results of the second operation i+3 will be returned by foreach. However, nothing gets populated in the list oper1! In this case, only the result of i+3 gets returned from the loop.
Is there any way of returning or saving both outputs in two separate lists?
Don't try to use side-effects with foreach or any other parallel program package. Instead, return all of the values from the body of the foreach loop in a list. If you want your final result to be a list of two lists rather than a list of 100,000 lists, then specify a combine function that transposes the results:
comb <- function(x, ...) {
lapply(seq_along(x),
function(i) c(x[[i]], lapply(list(...), function(y) y[[i]])))
}
oper <- foreach(i=1:10, .combine='comb', .multicombine=TRUE,
.init=list(list(), list())) %dopar% {
list(i+2, i+3)
}
oper1 <- oper[[1]]
oper2 <- oper[[2]]
Note that this combine function requires the use of the .init argument to set the value of x for the first invocation of the combine function.
I prefer to use a class to hold multiple results for a %dopar% loop.
This example spins up 3 cores, calculates multiple results on each core, then returns the list of results to the calling thread.
Tested under RStudio, Windows 10, and R v3.3.2.
library(foreach)
library(doParallel)
# Create class which holds multiple results for each loop iteration.
# Each loop iteration populates two properties: $result1 and $result2.
# For a great tutorial on S3 classes, see:
# http://www.cyclismo.org/tutorial/R/s3Classes.html#creating-an-s3-class
multiResultClass <- function(result1=NULL,result2=NULL)
{
me <- list(
result1 = result1,
result2 = result2
)
## Set the name for the class
class(me) <- append(class(me),"multiResultClass")
return(me)
}
cl <- makeCluster(3)
registerDoParallel(cl)
oper <- foreach(i=1:10) %dopar% {
result <- multiResultClass()
result$result1 <- i+1
result$result2 <- i+2
return(result)
}
stopCluster(cl)
oper1 <- oper[[1]]$result1
oper2 <- oper[[1]]$result2
This toy example shows how to return multiple results from a %dopar% loop.
This example:
Spins up 3 cores.
Renders a graph on each core.
Returns the graph and an attached message.
Prints the graphs and it's attached message out.
I found this really useful to speed up using Rmarkdown to print 1,800 graphs into a PDF document.
Tested under Windows 10, RStudio, and R v3.3.2.
R code:
# Demo of returning multiple results from a %dopar% loop.
library(foreach)
library(doParallel)
library(ggplot2)
cl <- makeCluster(3)
registerDoParallel(cl)
# Create class which holds multiple results for each loop iteration.
# Each loop iteration populates two properties: $resultPlot and $resultMessage.
# For a great tutorial on S3 classes, see:
# http://www.cyclismo.org/tutorial/R/s3Classes.html#creating-an-s3-class
plotAndMessage <- function(resultPlot=NULL,resultMessage="?")
{
me <- list(
resultPlot = resultPlot,
resultMessage = resultMessage
)
# Set the name for the class
class(me) <- append(class(me),"plotAndMessage")
return(me)
}
oper <- foreach(i=1:5, .packages=c("ggplot2")) %dopar% {
x <- c(i:(i+2))
y <- c(i:(i+2))
df <- data.frame(x,y)
p <- ggplot(df, aes(x,y))
p <- p + geom_point()
message <- paste("Hello, world! i=",i,"\n",sep="")
result <- plotAndMessage()
result$resultPlot <- p
result$resultMessage <- message
return(result)
}
# Print resultant plots and messages. Despite running on multiple cores,
# 'foreach' guarantees that the plots arrive back in the original order.
foreach(i=1:5) %do% {
# Print message attached to plot.
cat(oper[[i]]$resultMessage)
# Print plot.
print(oper[[i]]$resultPlot)
}
stopCluster(cl)