Vectorizing a for-loop for cross-validation in R

Charles writes about saving execution time by eliminating redundancies.
If you see redundancies related to calling a time-consuming function
multiple times with the same arguments, a very easy way to speed up your
program is to memoise the functions using the package memoise.

HTH,
Eric




On Wed, Jan 23, 2019 at 8:34 PM Berry, Charles <ccberry at ucsd.edu> wrote:

See inline.

On Jan 23, 2019, at 2:17 AM, Aleksandre Gavashelishvili <

aleksandre.gavashelishvili at iliauni.edu.ge> wrote:

I'm trying to speed up a script that otherwise takes days to handle

larger

data sets. So, is there a way to completely vectorize or paralellize the
following script:

               *# k-fold cross validation*

df <- trees # a data frame 'trees' from R.
df <- df[sample(nrow(df)), ] # randomly shuffles the data.
k <- 10 # Number of folds. Note k=nrow(df) in the leave-one-out cross
validation.
folds <- cut(seq(from=1, to=nrow(df)), breaks=k, labels=FALSE) # creates
unique numbers for k equally size folds.
df$ID <- folds # adds fold IDs.
df[paste("pred", 1:3, sep="")] <- NA # adds multiple columns "pred1"
"pred2" "pred3" to speed up the following loop.

library(mgcv)

Rprof()

replicate(100, {

for(i in 1:k) {
 # looping for different models:
 m1 <- gam(Volume ~ s(Height), data=df, subset=(ID != i))
 m2 <- gam(Volume ~ s(Girth), data=df, subset=(ID != i))
 m3 <- gam(Volume ~ s(Girth) + s(Height), data=df, subset=(ID != i))

 # looping for predictions:
 df[df$ID==i, "pred1"] <- predict(m1, df[df$ID==i, ], type="response")
 df[df$ID==i, "pred2"] <- predict(m2, df[df$ID==i, ], type="response")
 df[df$ID==i, "pred3"] <- predict(m3, df[df$ID==i, ], type="response")
}

})

Rprof(NULL)

summaryRprof()

## read ?Rprof to get a sense of what it does

## read the summary to determine where time is being spent.

## the result was surprising to me. YMMV.

## there may be redundancies that you can eliminate by
##  - doing the setup within gam() one time and saving it
##  - calling the worker functions by modifying the setup
##    in a loop or function and saving the results

# calculating residuals:
df$res1 <- with(df, Volume - pred1)
df$res2 <- with(df, Volume - pred2)
df$res3 <- with(df, Volume - pred3)

Model <- paste("m", 1:3, sep="") # creates a vector of model names.

# creating a vector of mean-square errors (MSE):
MSE <- with(df, c(
 sum(res1^2) / nrow(df),
 sum(res2^2) / nrow(df),
 sum(res3^2) / nrow(df)
))

model.mse <- data.frame(Model, MSE) # creates a data frame of model

names

and mean-square errors.
model.mse <- model.mse[order(model.mse$MSE), ] # rearranges the previous
data frame in order of increasing mean-square errors.

I'd appreciate any help. This code takes several days if run on >=30,000
different GAM models and 3 predictors. Could you please help with
re-writing the script into sapply() or foreach()/doParallel format?

This is something you should learn to do. It is pretty standard practice.
Use the body of your for loop as the body of a function, add arguments, and
create a suitable return value. The something like

        lapply( 1:k, your.loop.body.function, other.arg1, other.arg2, ...)

should work.  If it does, then parallel::mclapply(...) should also work.

HTH,

Chuck