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 <ccbe...@ucsd.edu> wrote:
> See inline.
>
> > On Jan 23, 2019, at 2:17 AM, Aleksandre Gavashelishvili <
> aleksandre.gavashelishv...@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
>
>
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