What tricks do people use to manage the available memory of an interactive R session? I use the functions below [based on postings by Petr Pikal and David Hinds to the r-help list in 2004] to list (and/or sort) the largest objects and to occassionally rm() some of them. But by far the most effective solution was ... to run under 64-bit Linux with ample memory.
Any other nice tricks folks want to share? One per post, please.
# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
decreasing=FALSE, head=FALSE, n=5) {
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.size <- napply(names, object.size)
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size, obj.dim)
names(out) <- c("Type", "Size", "Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
if (head)
out <- head(out, n)
out
}
# shorthand
lsos <- function(..., n=10) {
.ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}
Ensure you record your work in a reproducible script. From time-to-time, reopen R, then source() your script. You'll clean out anything you're no longer using, and as an added benefit will have tested your code.
I use the data.table package. With its := operator you can :
Add columns by reference
Modify subsets of existing columns by reference, and by group by reference
Delete columns by reference
None of these operations copy the (potentially large) data.table at all, not even once.
Aggregation is also particularly fast because data.table uses much less working memory.
Related links :
News from data.table, London R presentation, 2012
When should I use the := operator in data.table?
Saw this on a twitter post and think it's an awesome function by Dirk! Following on from JD Long's answer, I would do this for user friendly reading:
# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
decreasing=FALSE, head=FALSE, n=5) {
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.prettysize <- napply(names, function(x) {
format(utils::object.size(x), units = "auto") })
obj.size <- napply(names, object.size)
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size, obj.prettysize, obj.dim)
names(out) <- c("Type", "Size", "PrettySize", "Length/Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
if (head)
out <- head(out, n)
out
}
# shorthand
lsos <- function(..., n=10) {
.ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}
lsos()
Which results in something like the following:
Type Size PrettySize Length/Rows Columns
pca.res PCA 790128 771.6 Kb 7 NA
DF data.frame 271040 264.7 Kb 669 50
factor.AgeGender factanal 12888 12.6 Kb 12 NA
dates data.frame 9016 8.8 Kb 669 2
sd. numeric 3808 3.7 Kb 51 NA
napply function 2256 2.2 Kb NA NA
lsos function 1944 1.9 Kb NA NA
load loadings 1768 1.7 Kb 12 2
ind.sup integer 448 448 bytes 102 NA
x character 96 96 bytes 1 NA
NOTE: The main part I added was (again, adapted from JD's answer) :
obj.prettysize <- napply(names, function(x) {
print(object.size(x), units = "auto") })
I make aggressive use of the subset parameter with selection of only the required variables when passing dataframes to the data= argument of regression functions. It does result in some errors if I forget to add variables to both the formula and the select= vector, but it still saves a lot of time due to decreased copying of objects and reduces the memory footprint significantly. Say I have 4 million records with 110 variables (and I do.) Example:
# library(rms); library(Hmisc) for the cph,and rcs functions
Mayo.PrCr.rbc.mdl <-
cph(formula = Surv(surv.yr, death) ~ age + Sex + nsmkr + rcs(Mayo, 4) +
rcs(PrCr.rat, 3) + rbc.cat * Sex,
data = subset(set1HLI, gdlab2 & HIVfinal == "Negative",
select = c("surv.yr", "death", "PrCr.rat", "Mayo",
"age", "Sex", "nsmkr", "rbc.cat")
) )
By way of setting context and the strategy: the gdlab2 variable is a logical vector that was constructed for subjects in a dataset that had all normal or almost normal values for a bunch of laboratory tests and HIVfinal was a character vector that summarized preliminary and confirmatory testing for HIV.
I love Dirk's .ls.objects() script but I kept squinting to count characters in the size column. So I did some ugly hacks to make it present with pretty formatting for the size:
.ls.objects <- function (pos = 1, pattern, order.by,
decreasing=FALSE, head=FALSE, n=5) {
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.size <- napply(names, object.size)
obj.prettysize <- sapply(obj.size, function(r) prettyNum(r, big.mark = ",") )
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size,obj.prettysize, obj.dim)
names(out) <- c("Type", "Size", "PrettySize", "Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
out <- out[c("Type", "PrettySize", "Rows", "Columns")]
names(out) <- c("Type", "Size", "Rows", "Columns")
if (head)
out <- head(out, n)
out
}
That's a good trick.
One other suggestion is to use memory efficient objects wherever possible: for instance, use a matrix instead of a data.frame.
This doesn't really address memory management, but one important function that isn't widely known is memory.limit(). You can increase the default using this command, memory.limit(size=2500), where the size is in MB. As Dirk mentioned, you need to be using 64-bit in order to take real advantage of this.
I quite like the improved objects function developed by Dirk. Much of the time though, a more basic output with the object name and size is sufficient for me. Here's a simpler function with a similar objective. Memory use can be ordered alphabetically or by size, can be limited to a certain number of objects, and can be ordered ascending or descending. Also, I often work with data that are 1GB+, so the function changes units accordingly.
showMemoryUse <- function(sort="size", decreasing=FALSE, limit) {
objectList <- ls(parent.frame())
oneKB <- 1024
oneMB <- 1048576
oneGB <- 1073741824
memoryUse <- sapply(objectList, function(x) as.numeric(object.size(eval(parse(text=x)))))
memListing <- sapply(memoryUse, function(size) {
if (size >= oneGB) return(paste(round(size/oneGB,2), "GB"))
else if (size >= oneMB) return(paste(round(size/oneMB,2), "MB"))
else if (size >= oneKB) return(paste(round(size/oneKB,2), "kB"))
else return(paste(size, "bytes"))
})
memListing <- data.frame(objectName=names(memListing),memorySize=memListing,row.names=NULL)
if (sort=="alphabetical") memListing <- memListing[order(memListing$objectName,decreasing=decreasing),]
else memListing <- memListing[order(memoryUse,decreasing=decreasing),] #will run if sort not specified or "size"
if(!missing(limit)) memListing <- memListing[1:limit,]
print(memListing, row.names=FALSE)
return(invisible(memListing))
}
And here is some example output:
> showMemoryUse(decreasing=TRUE, limit=5)
objectName memorySize
coherData 713.75 MB
spec.pgram_mine 149.63 kB
stoch.reg 145.88 kB
describeBy 82.5 kB
lmBandpass 68.41 kB
I never save an R workspace. I use import scripts and data scripts and output any especially large data objects that I don't want to recreate often to files. This way I always start with a fresh workspace and don't need to clean out large objects. That is a very nice function though.
Unfortunately I did not have time to test it extensively but here is a memory tip that I have not seen before. For me the required memory was reduced with more than 50%.
When you read stuff into R with for example read.csv they require a certain amount of memory.
After this you can save them with save("Destinationfile",list=ls())
The next time you open R you can use load("Destinationfile")
Now the memory usage might have decreased.
It would be nice if anyone could confirm whether this produces similar results with a different dataset.
To further illustrate the common strategy of frequent restarts, we can use littler which allows us to run simple expressions directly from the command-line. Here is an example I sometimes use to time different BLAS for a simple crossprod.
r -e'N<-3*10^3; M<-matrix(rnorm(N*N),ncol=N); print(system.time(crossprod(M)))'
Likewise,
r -lMatrix -e'example(spMatrix)'
loads the Matrix package (via the --packages | -l switch) and runs the examples of the spMatrix function. As r always starts 'fresh', this method is also a good test during package development.
Last but not least r also work great for automated batch mode in scripts using the '#!/usr/bin/r' shebang-header. Rscript is an alternative where littler is unavailable (e.g. on Windows).
For both speed and memory purposes, when building a large data frame via some complex series of steps, I'll periodically flush it (the in-progress data set being built) to disk, appending to anything that came before, and then restart it. This way the intermediate steps are only working on smallish data frames (which is good as, e.g., rbind slows down considerably with larger objects). The entire data set can be read back in at the end of the process, when all the intermediate objects have been removed.
dfinal <- NULL
first <- TRUE
tempfile <- "dfinal_temp.csv"
for( i in bigloop ) {
if( !i %% 10000 ) {
print( i, "; flushing to disk..." )
write.table( dfinal, file=tempfile, append=!first, col.names=first )
first <- FALSE
dfinal <- NULL # nuke it
}
# ... complex operations here that add data to 'dfinal' data frame
}
print( "Loop done; flushing to disk and re-reading entire data set..." )
write.table( dfinal, file=tempfile, append=TRUE, col.names=FALSE )
dfinal <- read.table( tempfile )
Just to note that data.table package's tables() seems to be a pretty good replacement for Dirk's .ls.objects() custom function (detailed in earlier answers), although just for data.frames/tables and not e.g. matrices, arrays, lists.
I'm fortunate and my large data sets are saved by the instrument in "chunks" (subsets) of roughly 100 MB (32bit binary). Thus I can do pre-processing steps (deleting uninformative parts, downsampling) sequentially before fusing the data set.
Calling gc () "by hand" can help if the size of the data get close to available memory.
Sometimes a different algorithm needs much less memory.
Sometimes there's a trade off between vectorization and memory use.
compare: split & lapply vs. a for loop.
For the sake of fast & easy data analysis, I often work first with a small random subset (sample ()) of the data. Once the data analysis script/.Rnw is finished data analysis code and the complete data go to the calculation server for over night / over weekend / ... calculation.
The use of environments instead of lists to handle collections of objects which occupy a significant amount of working memory.
The reason: each time an element of a list structure is modified, the whole list is temporarily duplicated. This becomes an issue if the storage requirement of the list is about half the available working memory, because then data has to be swapped to the slow hard disk. Environments, on the other hand, aren't subject to this behaviour and they can be treated similar to lists.
Here is an example:
get.data <- function(x)
{
# get some data based on x
return(paste("data from",x))
}
collect.data <- function(i,x,env)
{
# get some data
data <- get.data(x[[i]])
# store data into environment
element.name <- paste("V",i,sep="")
env[[element.name]] <- data
return(NULL)
}
better.list <- new.env()
filenames <- c("file1","file2","file3")
lapply(seq_along(filenames),collect.data,x=filenames,env=better.list)
# read/write access
print(better.list[["V1"]])
better.list[["V2"]] <- "testdata"
# number of list elements
length(ls(better.list))
In conjunction with structures such as big.matrix or data.table which allow for altering their content in-place, very efficient memory usage can be achieved.
The llfunction in gData package can show the memory usage of each object as well.
gdata::ll(unit='MB')
If you really want to avoid the leaks, you should avoid creating any big objects in the global environment.
What I usually do is to have a function that does the job and returns NULL — all data is read and manipulated in this function or others that it calls.
With only 4GB of RAM (running Windows 10, so make that about 2 or more realistically 1GB) I've had to be real careful with the allocation.
I use data.table almost exclusively.
The 'fread' function allows you to subset information by field names on import; only import the fields that are actually needed to begin with. If you're using base R read, null the spurious columns immediately after import.
As 42- suggests, where ever possible I will then subset within the columns immediately after importing the information.
I frequently rm() objects from the environment as soon as they're no longer needed, e.g. on the next line after using them to subset something else, and call gc().
'fread' and 'fwrite' from data.table can be very fast by comparison with base R reads and writes.
As kpierce8 suggests, I almost always fwrite everything out of the environment and fread it back in, even with thousand / hundreds of thousands of tiny files to get through. This not only keeps the environment 'clean' and keeps the memory allocation low but, possibly due to the severe lack of RAM available, R has a propensity for frequently crashing on my computer; really frequently. Having the information backed up on the drive itself as the code progresses through various stages means I don't have to start right from the beginning if it crashes.
As of 2017, I think the fastest SSDs are running around a few GB per second through the M2 port. I have a really basic 50GB Kingston V300 (550MB/s) SSD that I use as my primary disk (has Windows and R on it). I keep all the bulk information on a cheap 500GB WD platter. I move the data sets to the SSD when I start working on them. This, combined with 'fread'ing and 'fwrite'ing everything has been working out great. I've tried using 'ff' but prefer the former. 4K read/write speeds can create issues with this though; backing up a quarter of a million 1k files (250MBs worth) from the SSD to the platter can take hours. As far as I'm aware, there isn't any R package available yet that can automatically optimise the 'chunkification' process; e.g. look at how much RAM a user has, test the read/write speeds of the RAM / all the drives connected and then suggest an optimal 'chunkification' protocol. This could produce some significant workflow improvements / resource optimisations; e.g. split it to ... MB for the ram -> split it to ... MB for the SSD -> split it to ... MB on the platter -> split it to ... MB on the tape. It could sample data sets beforehand to give it a more realistic gauge stick to work from.
A lot of the problems I've worked on in R involve forming combination and permutation pairs, triples etc, which only makes having limited RAM more of a limitation as they will often at least exponentially expand at some point. This has made me focus a lot of attention on the quality as opposed to quantity of information going into them to begin with, rather than trying to clean it up afterwards, and on the sequence of operations in preparing the information to begin with (starting with the simplest operation and increasing the complexity); e.g. subset, then merge / join, then form combinations / permutations etc.
There do seem to be some benefits to using base R read and write in some instances. For instance, the error detection within 'fread' is so good it can be difficult trying to get really messy information into R to begin with to clean it up. Base R also seems to be a lot easier if you're using Linux. Base R seems to work fine in Linux, Windows 10 uses ~20GB of disc space whereas Ubuntu only needs a few GB, the RAM needed with Ubuntu is slightly lower. But I've noticed large quantities of warnings and errors when installing third party packages in (L)Ubuntu. I wouldn't recommend drifting too far away from (L)Ubuntu or other stock distributions with Linux as you can loose so much overall compatibility it renders the process almost pointless (I think 'unity' is due to be cancelled in Ubuntu as of 2017). I realise this won't go down well with some Linux users but some of the custom distributions are borderline pointless beyond novelty (I've spent years using Linux alone).
Hopefully some of that might help others out.
This is a newer answer to this excellent old question. From Hadley's Advanced R:
install.packages("pryr")
library(pryr)
object_size(1:10)
## 88 B
object_size(mean)
## 832 B
object_size(mtcars)
## 6.74 kB
(http://adv-r.had.co.nz/memory.html)
This adds nothing to the above, but is written in the simple and heavily commented style that I like. It yields a table with the objects ordered in size , but without some of the detail given in the examples above:
#Find the objects
MemoryObjects = ls()
#Create an array
MemoryAssessmentTable=array(NA,dim=c(length(MemoryObjects),2))
#Name the columns
colnames(MemoryAssessmentTable)=c("object","bytes")
#Define the first column as the objects
MemoryAssessmentTable[,1]=MemoryObjects
#Define a function to determine size
MemoryAssessmentFunction=function(x){object.size(get(x))}
#Apply the function to the objects
MemoryAssessmentTable[,2]=t(t(sapply(MemoryAssessmentTable[,1],MemoryAssessmentFunction)))
#Produce a table with the largest objects first
noquote(MemoryAssessmentTable[rev(order(as.numeric(MemoryAssessmentTable[,2]))),])
As well as the more general memory management techniques given in the answers above, I always try to reduce the size of my objects as far as possible. For example, I work with very large but very sparse matrices, in other words matrices where most values are zero. Using the 'Matrix' package (capitalisation important) I was able to reduce my average object sizes from ~2GB to ~200MB as simply as:
my.matrix <- Matrix(my.matrix)
The Matrix package includes data formats that can be used exactly like a regular matrix (no need to change your other code) but are able to store sparse data much more efficiently, whether loaded into memory or saved to disk.
Additionally, the raw files I receive are in 'long' format where each data point has variables x, y, z, i. Much more efficient to transform the data into an x * y * z dimension array with only variable i.
Know your data and use a bit of common sense.
If you are working on Linux and want to use several processes and only have to do read operations on one or more large objects use makeForkCluster instead of a makePSOCKcluster. This also saves you the time sending the large object to the other processes.
I really appreciate some of the answers above, following #hadley and #Dirk that suggest closing R and issuing source and using command line I come up with a solution that worked very well for me. I had to deal with hundreds of mass spectras, each occupies around 20 Mb of memory so I used two R scripts, as follows:
First a wrapper:
#!/usr/bin/Rscript --vanilla --default-packages=utils
for(l in 1:length(fdir)) {
for(k in 1:length(fds)) {
system(paste("Rscript runConsensus.r", l, k))
}
}
with this script I basically control what my main script do runConsensus.r, and I write the data answer for the output. With this, each time the wrapper calls the script it seems the R is reopened and the memory is freed.
Hope it helps.
Tip for dealing with objects requiring heavy intermediate calculation: When using objects that require a lot of heavy calculation and intermediate steps to create, I often find it useful to write a chunk of code with the function to create the object, and then a separate chunk of code that gives me the option either to generate and save the object as an rmd file, or load it externally from an rmd file I have already previously saved. This is especially easy to do in R Markdown using the following code-chunk structure.
```{r Create OBJECT}
COMPLICATED.FUNCTION <- function(...) { Do heavy calculations needing lots of memory;
Output OBJECT; }
```
```{r Generate or load OBJECT}
LOAD <- TRUE
SAVE <- TRUE
#NOTE: Set LOAD to TRUE if you want to load saved file
#NOTE: Set LOAD to FALSE if you want to generate the object from scratch
#NOTE: Set SAVE to TRUE if you want to save the object externally
if(LOAD) {
OBJECT <- readRDS(file = 'MySavedObject.rds')
} else {
OBJECT <- COMPLICATED.FUNCTION(x, y, z)
if (SAVE) { saveRDS(file = 'MySavedObject.rds', object = OBJECT) } }
```
With this code structure, all I need to do is to change LOAD depending on whether I want to generate the object, or load it directly from an existing saved file. (Of course, I have to generate it and save it the first time, but after this I have the option of loading it.) Setting LOAD <- TRUE bypasses use of my complicated function and avoids all of the heavy computation therein. This method still requires enough memory to store the object of interest, but it saves you from having to calculate it each time you run your code. For objects that require a lot of heavy calculation of intermediate steps (e.g., for calculations involving loops over large arrays) this can save a substantial amount of time and computation.
Running
for (i in 1:10)
gc(reset = T)
from time to time also helps R to free unused but still not released memory.
You also can get some benefit using knitr and puting your script in Rmd chuncks.
I usually divide the code in different chunks and select which one will save a checkpoint to cache or to a RDS file, and
Over there you can set a chunk to be saved to "cache", or you can decide to run or not a particular chunk. In this way, in a first run you can process only "part 1", another execution you can select only "part 2", etc.
Example:
part1
```{r corpus, warning=FALSE, cache=TRUE, message=FALSE, eval=TRUE}
corpusTw <- corpus(twitter) # build the corpus
```
part2
```{r trigrams, warning=FALSE, cache=TRUE, message=FALSE, eval=FALSE}
dfmTw <- dfm(corpusTw, verbose=TRUE, removeTwitter=TRUE, ngrams=3)
```
As a side effect, this also could save you some headaches in terms of reproducibility :)
Based on #Dirk's and #Tony's answer I have made a slight update. The result was outputting [1] before the pretty size values, so I took out the capture.output which solved the problem:
.ls.objects <- function (pos = 1, pattern, order.by,
decreasing=FALSE, head=FALSE, n=5) {
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.prettysize <- napply(names, function(x) {
format(utils::object.size(x), units = "auto") })
obj.size <- napply(names, utils::object.size)
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size, obj.prettysize, obj.dim)
names(out) <- c("Type", "Size", "PrettySize", "Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
if (head)
out <- head(out, n)
return(out)
}
# shorthand
lsos <- function(..., n=10) {
.ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}
lsos()
I try to keep the amount of objects small when working in a larger project with a lot of intermediate steps. So instead of creating many unique objects called
dataframe-> step1 -> step2 -> step3 -> result
raster-> multipliedRast -> meanRastF -> sqrtRast -> resultRast
I work with temporary objects that I call temp.
dataframe -> temp -> temp -> temp -> result
Which leaves me with less intermediate files and more overview.
raster <- raster('file.tif')
temp <- raster * 10
temp <- mean(temp)
resultRast <- sqrt(temp)
To save more memory I can simply remove temp when no longer needed.
rm(temp)
If I need several intermediate files, I use temp1, temp2, temp3.
For testing I use test, test2, ...
rm(list=ls()) is a great way to keep you honest and keep things reproducible.
Related
I have 4 subfolder that contains 5 rasters with continuous values. So a build a loop with "for" function to :
list these raster files
stack these files per folder , i.e 4 rasterstacks objects (that contains 5 rasters)
I apllied a treshold to transform the the continuous raster in binary raster
Finally I wrote the binary raster using wirte.raster function.
My issue is in a step 4. Eventhough I use the argument "byLayer = T" in writeRaster function
the rasters saved were a rasterstack with the 5 binary rasters. And i want write it per raster, per file, per band
I really grateful if anyone give me any insights
setwd("Vole_raw_mean_Present/")
sub <- list.dirs(full.names=FALSE, recursive=FALSE)
for(j in 1:length(sub)) {
print(sub[j])
h <- list.files(path=sub[j], recursive=TRUE, full.names=TRUE, pattern='.tif')
print(h)
stack_present <- stack(h)
print(stack_present)
binary_0.2 <- stack_present >=0.2
writeRaster(binary_0.2, filename=paste0(sub[j], bylayer = T, suffix = "_bin.tif"), overwrite=TRUE)
}
This is wrong because the argument "bylayer" is lost as it becomes part of the filename)
writeRaster(binary_0.2, filename=paste0(sub[j], bylayer = T, suffix = "_bin.tif"), overwrite=TRUE)
It should be something like this (and it helps to do it in two steps)
f <- paste0(sub[j], "bin.tif")
writeRaster(binary_0.2, filename=f, bylayer=TRUE, overwrite=TRUE)
Illustrated here
library(raster)
b <- brick(system.file("external/rlogo.grd", package="raster"))
dir.create("test")
setwd("test")
writeRaster(b, filename="abc.tif", bylayer=T)
list.files()
#[1] "abc_1.tif" "abc_2.tif" "abc_3.tif"
writeRaster(b, filename="bin.tif", bylayer=T, suffix = paste0("f", 1:3))
list.files(pattern="bin")
#[1] "bin_f1.tif" "bin_f2.tif" "bin_f3.tif"
Alternatively, you can loop over the files within each folder
as i mentioned on subject of this post i found out OOP is slower than Structural Programming(spaghetti code) in the hard way.
i writed a simulated annealing program with OOP then remove one class and write it structural in main form. suddenly it got much faster . i was calling my removed class in every iteration in OOP program.
also checked it with Tabu Search. Same result .
can anyone tell me why this is happening and how can i fix it on other OOP programs?
are there any tricks ? for example cache my classes or something like that?
(Programs has been written in C#)
If you have a high-frequency loop, and inside that loop you create new objects and don't call other functions very much, then, yes, you will see that if you can avoid those news, say by re-using one copy of the object, you can save a large fraction of total time.
Between new, constructors, destructors, and garbage collection, a very little code can waste a whole lot of time.
Use them sparingly.
Memory access is often overlooked. The way o.o. tends to lay out data in memory is not conducive to efficient memory access in practice in loops. Consider the following pseudocode:
adult_clients = 0
for client in list_of_all_clients:
if client.age >= AGE_OF_MAJORITY:
adult_clients++
It so happens that the way this is accessed from memory is quite inefficient on modern architectures because they like accessing large contiguous rows of memory, but we only care for client.age, and of all clients we have; those will not be laid out in contiguous memory.
Focusing on objects that have fields results into data being laid out in memory in such a way that fields that hold the same type of information will not be laid out in consecutive memory. Performance-heavy code tends to involve loops that often look at data with the same conceptual meaning. It is conducive to performance that such data be laid out in contiguous memory.
Consider these two examples in Rust:
// struct that contains an id, and an optiona value of whether the id is divisible by three
struct Foo {
id : u32,
divbythree : Option<bool>,
}
fn main () {
// create a pretty big vector of these structs with increasing ids, and divbythree initialized as None
let mut vec_of_foos : Vec<Foo> = (0..100000000).map(|i| Foo{ id : i, divbythree : None }).collect();
// loop over all hese vectors, determine if the id is divisible by three
// and set divbythree accordingly
let mut divbythrees = 0;
for foo in vec_of_foos.iter_mut() {
if foo.id % 3 == 0 {
foo.divbythree = Some(true);
divbythrees += 1;
} else {
foo.divbythree = Some(false);
}
}
// print the number of times it was divisible by three
println!("{}", divbythrees);
}
On my system, the real time with rustc -O is 0m0.436s; now let us consider this example:
fn main () {
// this time we create two vectors rather than a vector of structs
let vec_of_ids : Vec<u32> = (0..100000000).collect();
let mut vec_of_divbythrees : Vec<Option<bool>> = vec![None; vec_of_ids.len()];
// but we basically do the same thing
let mut divbythrees = 0;
for i in 0..vec_of_ids.len(){
if vec_of_ids[i] % 3 == 0 {
vec_of_divbythrees[i] = Some(true);
divbythrees += 1;
} else {
vec_of_divbythrees[i] = Some(false);
}
}
println!("{}", divbythrees);
}
This runs in 0m0.254s on the same optimization level, — close to half the time needed.
Despite having to allocate two vectors instead of of one, storing similar values in contiguous memory has almost halved the execution time. Though obviously the o.o. approach provides for much nicer and more maintainable code.
P.s.: it occurs to me that I should probably explain why this matters so much given that the code itself in both cases still indexes memory one field at a time, rather than, say, putting a large swath on the stack. The reason is c.p.u. caches: when the program asks for the memory at a certain address, it actually obtains, and caches, a significant chunk of memory around that address, and if memory next to it be asked quickly again, then it can serve it from the cache, rather than from actual physical working memory. Of course, compilers will also vectorize the bottom code more efficiently as a consequence.
In Revolution R 2.12.2 on Windows 7 and Ubuntu 64-bit 11.04 I have a data frame with over 100K rows and over 100 columns, and I derive ~5 columns (sqrt, log, log10, etc) for each of the original columns and add them to the same data frame. Without parallelism using foreach and %do%, this works fine, but it's slow. When I try to parallelize it with foreach and %dopar%, it will not access the global environment (to prevent race conditions or something like that), so I cannot modify the data frame because the data frame object is 'not found.'
My question is how can I make this faster? In other words, how to parallelize either the columns or the transformations?
Simplified example:
require(foreach)
require(doSMP)
w <- startWorkers()
registerDoSMP(w)
transform_features <- function()
{
cols<-c(1,2,3,4) # in my real code I select certain columns (not all)
foreach(thiscol=cols, mydata) %dopar% {
name <- names(mydata)[thiscol]
print(paste('transforming variable ', name))
mydata[,paste(name, 'sqrt', sep='_')] <<- sqrt(mydata[,thiscol])
mydata[,paste(name, 'log', sep='_')] <<- log(mydata[,thiscol])
}
}
n<-10 # I often have 100K-1M rows
mydata <- data.frame(
a=runif(n,1,100),
b=runif(n,1,100),
c=runif(n,1,100),
d=runif(n,1,100)
)
ncol(mydata) # 4 columns
transform_features()
ncol(mydata) # if it works, there should be 8
Notice if you change %dopar% to %do% it works fine
Try the := operator in data.table to add the columns by reference. You'll need with=FALSE so you can put the call to paste on the LHS of :=.
See When should I use the := operator in data.table?
Might it be easier if you did something like
n<-10
mydata <- data.frame(
a=runif(n,1,100),
b=runif(n,1,100),
c=runif(n,1,100),
d=runif(n,1,100)
)
mydata_sqrt <- sqrt(mydata)
colnames(mydata_sqrt) <- paste(colnames(mydata), 'sqrt', sep='_')
mydata <- cbind(mydata, mydata_sqrt)
producing something like
> mydata
a b c d a_sqrt b_sqrt c_sqrt d_sqrt
1 29.344088 47.232144 57.218271 58.11698 5.417018 6.872565 7.564276 7.623449
2 5.037735 12.282458 3.767464 40.50163 2.244490 3.504634 1.940996 6.364089
3 80.452595 76.756839 62.128892 43.84214 8.969537 8.761098 7.882188 6.621340
4 39.250277 11.488680 38.625132 23.52483 6.265004 3.389496 6.214912 4.850240
5 11.459075 8.126104 29.048527 76.17067 3.385126 2.850632 5.389669 8.727581
6 26.729365 50.140679 49.705432 57.69455 5.170045 7.081008 7.050208 7.595693
7 42.533937 7.481240 59.977556 11.80717 6.521805 2.735186 7.744518 3.436157
8 41.673752 89.043099 68.839051 96.15577 6.455521 9.436265 8.296930 9.805905
9 59.122106 74.308573 69.883037 61.85404 7.689090 8.620242 8.359607 7.864734
10 24.191878 94.059012 46.804937 89.07993 4.918524 9.698403 6.841413 9.438217
There are two ways you can handle this:
Loop over each column (or, better yet, a subset of the columns) and apply the transformations to create a temporary data frame, return that, and then do cbind of the list of data frames, as #Henry suggested.
Loop over the transformations, apply each to the data frame, and then return the transformation data frames, cbind, and proceed.
Personally, the way I tend to do things like this is create a bigmatrix object (either in memory or on disk, using the bigmemory package), and you can access all of the columns in shared memory. Just pre-allocate the columns you will fill in, and you won't need to do a post hoc cbind. I tend to do it on disk. Just be sure to run flush(), to make sure everything is written to disk.
I am trying to reshape a data frame in R and it seems to have problems using the recommended ways of doing so. The data frame has the following structure:
ID DATE1 DATE2 VALTYPE VALUE
'abcd1233' 2009-11-12 2009-12-23 'TYPE1' 123.45
...
VALTYPE is a string and is a factor with only 2 values (say TYPE1 and TYPE2). I need to transform it into the following data frame ("wide" transpose) based on common ID and DATEs:
ID DATE1 DATE2 VALUE.TYPE1 VALUE.TYPE2
'abcd1233' 2009-11-12 2009-12-23 123.45 NA
...
The data frame has more than 4,500,000 observations (although about 70% of VALUEs are NA). The machine is an Intel-based Linux workstation with 4Gb of RAM. Loading the data (from a compressed Rdata file) into a fresh R process makes it grow to about 250Mb which clearly leaves a lot of space for reshaping.
These are my experiences so far:
Using vanilla reshape() method:
tbl2 <- reshape(tbl, direction = "wide", idvar = c("ID", "DATE1", "DATE2"),
timevar = "VALTYPE");
RESULT: Error: cannot allocate vector of size 4.8 Gb
Using cast() method of reshape package:
tbl2 <- cast(tbl, ID + DATE1 + DATE2 ~ VALTYPE);
RESULT: R process consumes all RAM with no end in sight. Had to kill the process eventually.
Using by() and merge():
sp <- by(tbl[c(1,2,3,5)], tbl$VALTYPE, function(x) x);
tbl <- merge(sp[["TYPE1"]], sp[["TYPE2"]],
by = c("ID", "DATE1", "DATE2"), all = TRUE, sort = TRUE);
RESULT: works fine, although this is not very elegant and foolproof (i.e. it will break if more types are added).
To add insult to injury, the operation in question can be trivially achieved in about 3 lines of AWK or Perl (and with hardly any RAM used). So the question is: what is a better way to do this operation in R using recommended methods without consuming all available RAM?
A useful trick is to combine the id variables into a character vector and then do the reshape.
tbl$NEWID <- with(tbl, paste(ID, DATE1, DATE2, sep=";"))
tbl2 <- recast(tbl2, NEWID ~ VALTYPE, measure.var="VALUE")
It's about 40% faster in a problem of similar size in my intel core2 duo 2.2ghz macbook.
What about doing this in a non-R-like manner? I assume you have a TYPE1 and a TYPE2 row for each value of ID,DATE1,DATE2? Then sort the dataframe by those variables, and write a big for loop. You can repeatedly do rbind() operations to build the table, or you could try to pre-allocate the table (maybe) and just assign the VALUE.TYPE1 and VALUE.TYPE2 slots with [<-, which should do the assignment in-place.
(Note that if you're using rbind(), I believe that it's inefficient if you have any factor variables, so make sure everything is a character instead!)
Maybe you could use the cat() function?
I wanted to compare the performance characteristics of immutable.Map and mutable.Map in Scala for a similar operation (namely, merging many maps into a single one. See this question). I have what appear to be similar implementations for both mutable and immutable maps (see below).
As a test, I generated a List containing 1,000,000 single-item Map[Int, Int] and passed this list into the functions I was testing. With sufficient memory, the results were unsurprising: ~1200ms for mutable.Map, ~1800ms for immutable.Map, and ~750ms for an imperative implementation using mutable.Map -- not sure what accounts for the huge difference there, but feel free to comment on that, too.
What did surprise me a bit, perhaps because I'm being a bit thick, is that with the default run configuration in IntelliJ 8.1, both mutable implementations hit an OutOfMemoryError, but the immutable collection did not. The immutable test did run to completion, but it did so very slowly -- it takes about 28 seconds. When I increased the max JVM memory (to about 200MB, not sure where the threshold is), I got the results above.
Anyway, here's what I really want to know:
Why do the mutable implementations run out of memory, but the immutable implementation does not? I suspect that the immutable version allows the garbage collector to run and free up memory before the mutable implementations do -- and all of those garbage collections explain the slowness of the immutable low-memory run -- but I'd like a more detailed explanation than that.
Implementations below. (Note: I don't claim that these are the best implementations possible. Feel free to suggest improvements.)
def mergeMaps[A,B](func: (B,B) => B)(listOfMaps: List[Map[A,B]]): Map[A,B] =
(Map[A,B]() /: (for (m <- listOfMaps; kv <-m) yield kv)) { (acc, kv) =>
acc + (if (acc.contains(kv._1)) kv._1 -> func(acc(kv._1), kv._2) else kv)
}
def mergeMutableMaps[A,B](func: (B,B) => B)(listOfMaps: List[mutable.Map[A,B]]): mutable.Map[A,B] =
(mutable.Map[A,B]() /: (for (m <- listOfMaps; kv <- m) yield kv)) { (acc, kv) =>
acc + (if (acc.contains(kv._1)) kv._1 -> func(acc(kv._1), kv._2) else kv)
}
def mergeMutableImperative[A,B](func: (B,B) => B)(listOfMaps: List[mutable.Map[A,B]]): mutable.Map[A,B] = {
val toReturn = mutable.Map[A,B]()
for (m <- listOfMaps; kv <- m) {
if (toReturn contains kv._1) {
toReturn(kv._1) = func(toReturn(kv._1), kv._2)
} else {
toReturn(kv._1) = kv._2
}
}
toReturn
}
Well, it really depends on what the actual type of Map you are using. Probably HashMap. Now, mutable structures like that gain performance by pre-allocating memory it expects to use. You are joining one million maps, so the final map is bound to be somewhat big. Let's see how these key/values get added:
protected def addEntry(e: Entry) {
val h = index(elemHashCode(e.key))
e.next = table(h).asInstanceOf[Entry]
table(h) = e
tableSize = tableSize + 1
if (tableSize > threshold)
resize(2 * table.length)
}
See the 2 * in the resize line? The mutable HashMap grows by doubling each time it runs out of space, while the immutable one is pretty conservative in memory usage (though existing keys will usually occupy twice the space when updated).
Now, as for other performance problems, you are creating a list of keys and values in the first two versions. That means that, before you join any maps, you already have each Tuple2 (the key/value pairs) in memory twice! Plus the overhead of List, which is small, but we are talking about more than one million elements times the overhead.
You may want to use a projection, which avoids that. Unfortunately, projection is based on Stream, which isn't very reliable for our purposes on Scala 2.7.x. Still, try this instead:
for (m <- listOfMaps.projection; kv <- m) yield kv
A Stream doesn't compute a value until it is needed. The garbage collector ought to collect the unused elements as well, as long as you don't keep a reference to the Stream's head, which seems to be the case in your algorithm.
EDIT
Complementing, a for/yield comprehension takes one or more collections and return a new collection. As often as it makes sense, the returning collection is of the same type as the original collection. So, for example, in the following code, the for-comprehension creates a new list, which is then stored inside l2. It is not val l2 = which creates the new list, but the for-comprehension.
val l = List(1,2,3)
val l2 = for (e <- l) yield e*2
Now, let's look at the code being used in the first two algorithms (minus the mutable keyword):
(Map[A,B]() /: (for (m <- listOfMaps; kv <-m) yield kv))
The foldLeft operator, here written with its /: synonymous, will be invoked on the object returned by the for-comprehension. Remember that a : at the end of an operator inverts the order of the object and the parameters.
Now, let's consider what object is this, on which foldLeft is being called. The first generator in this for-comprehension is m <- listOfMaps. We know that listOfMaps is a collection of type List[X], where X isn't really relevant here. The result of a for-comprehension on a List is always another List. The other generators aren't relevant.
So, you take this List, get all the key/values inside each Map which is a component of this List, and make a new List with all of that. That's why you are duplicating everything you have.
(in fact, it's even worse than that, because each generator creates a new collection; the collections created by the second generator are just the size of each element of listOfMaps though, and are immediately discarded after use)
The next question -- actually, the first one, but it was easier to invert the answer -- is how the use of projection helps.
When you call projection on a List, it returns new object, of type Stream (on Scala 2.7.x). At first you may think this will only make things worse, because you'll now have three copies of the List, instead of a single one. But a Stream is not pre-computed. It is lazily computed.
What that means is that the resulting object, the Stream, isn't a copy of the List, but, rather, a function that can be used to compute the Stream when required. Once computed, the result will be kept so that it doesn't need to be computed again.
Also, map, flatMap and filter of a Stream all return a new Stream, which means you can chain them all together without making a single copy of the List which created them. Since for-comprehensions with yield use these very functions, the use of Stream inside the prevent unnecessary copies of data.
Now, suppose you wrote something like this:
val kvs = for (m <- listOfMaps.projection; kv <-m) yield kv
(Map[A,B]() /: kvs) { ... }
In this case you aren't gaining anything. After assigning the Stream to kvs, the data hasn't been copied yet. Once the second line is executed, though, kvs will have computed each of its elements, and, therefore, will hold a complete copy of the data.
Now consider the original form::
(Map[A,B]() /: (for (m <- listOfMaps.projection; kv <-m) yield kv))
In this case, the Stream is used at the same time it is computed. Let's briefly look at how foldLeft for a Stream is defined:
override final def foldLeft[B](z: B)(f: (B, A) => B): B = {
if (isEmpty) z
else tail.foldLeft(f(z, head))(f)
}
If the Stream is empty, just return the accumulator. Otherwise, compute a new accumulator (f(z, head)) and then pass it and the function to the tail of the Stream.
Once f(z, head) has executed, though, there will be no remaining reference to the head. Or, in other words, nothing anywhere in the program will be pointing to the head of the Stream, and that means the garbage collector can collect it, thus freeing memory.
The end result is that each element produced by the for-comprehension will exist just briefly, while you use it to compute the accumulator. And this is how you save keeping a copy of your whole data.
Finally, there is the question of why the third algorithm does not benefit from it. Well, the third algorithm does not use yield, so no copy of any data whatsoever is being made. In this case, using projection only adds an indirection layer.