Install package ‘crossvalidation’

options(repos = c(
  techtonique = 'https://techtonique.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))

install.packages("crossvalidation")

Import packages

library(crossvalidation)
library(randomForest)
library(microbenchmark)

Input data

set.seed(123)
n <- 1000 ; p <- 10
X <- matrix(rnorm(n * p), n, p)
y <- rnorm(n)

Random forest hyperparameters for a grid search

tuning_grid <- base::expand.grid(mtry = c(2, 3, 4),
                                 ntree = c(100, 200, 300))
n_params <- nrow(tuning_grid)
print(tuning_grid)

Sequential and parallel execution of cross-validation on a tuning grid

n_cores <- 4

Sequential

f1 <- function() base::lapply(1:n_params,
                              function(i)
                                crossvalidation::crossval_ml(
                                  x = X,
                                  y = y,
                                  k = 5,
                                  repeats = 3,
                                  fit_func = randomForest::randomForest, 
                                  predict_func = predict,
                                  packages = "randomForest",
                                  fit_params = list(mtry = tuning_grid[i, "mtry"],
                                                    ntree = tuning_grid[i, "ntree"])
                                ))

Parallel 1

f2 <- function() parallel::mclapply(1:n_params,
                                    function(i)
                                      crossvalidation::crossval_ml(
                                        x = X,
                                        y = y,
                                        k = 5,
                                        repeats = 3,
                                        fit_func = randomForest::randomForest, 
                                        predict_func = predict,
                                        packages = "randomForest",
                                        fit_params = list(mtry = tuning_grid[i, "mtry"],
                                                          ntree = tuning_grid[i, "ntree"])
                                      ), mc.cores=n_cores)

Parallel 2

f3 <- function() base::lapply(1:n_params,
                              function(i)
                                crossvalidation::crossval_ml(
                                  x = X,
                                  y = y,
                                  k = 5,
                                  repeats = 3,
                                  fit_func = randomForest::randomForest, 
                                  predict_func = predict,
                                  packages = "randomForest",
                                  fit_params = list(mtry = tuning_grid[i, "mtry"],
                                                    ntree = tuning_grid[i, "ntree"]),
                                  cl=n_cores
                                ))

Check that the three functions return the same result

all.equal(f1(), f2())
all.equal(f2(), f3())

Timings for f1, f2, f3

(timings <- microbenchmark::microbenchmark(f1(), f2(), f3(), 
                                           times = 10L))

Plot results:

boxplot(timings, xlab = "function")

print(sessionInfo())

R version 4.0.4 (2021-02-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-7  randomForest_4.6-14   crossvalidation_0.3.0
[4] foreach_1.5.1         forecast_8.14         httr_1.4.2           

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        urca_1.3-0        pillar_1.4.6      compiler_4.0.4   
 [5] iterators_1.0.12  tseries_0.10-47   tools_4.0.4       xts_0.12.1       
 [9] digest_0.6.25     jsonlite_1.7.2    nlme_3.1-152      lifecycle_0.2.0  
[13] tibble_3.0.3      gtable_0.3.0      lattice_0.20-41   doSNOW_1.0.19    
[17] pkgconfig_2.0.3   rlang_0.4.10      rstudioapi_0.11   curl_4.3         
[21] parallel_4.0.4    dplyr_1.0.2       xml2_1.3.2        generics_0.0.2   
[25] vctrs_0.3.4       lmtest_0.9-38     grid_4.0.4        nnet_7.3-15      
[29] tidyselect_1.1.0  glue_1.4.2        R6_2.5.0          snow_0.4-3       
[33] crossval_0.2.1    farver_2.0.3      ggplot2_3.3.3     purrr_0.3.4      
[37] TTR_0.24.2        magrittr_1.5      codetools_0.2-18  scales_1.1.1     
[41] ellipsis_0.3.1    quantmod_0.4.17   mime_0.9          timeDate_3043.102
[45] colorspace_1.4-1  fracdiff_1.5-1    quadprog_1.5-8    munsell_0.5.0    
[49] crayon_1.3.4      zoo_1.8-8