mlr3resampling (version 2025.6.23)

proj_submit: Submit resampling split jobs in parallel

Description

Before running this function, you should define cluster.functions in your ~/.batchtools.conf.R file. It makes a batchtools registry, then runs batchtools::batchMap() and batchtools::submitJobs(); each iteration runs proj_compute_until_done.

Usage

proj_submit(
  proj_dir, tasks = 2, hours = 1, gigabytes = 1,
  verbose = FALSE, cluster.functions = NULL)

Value

The batchtools registry.

Arguments

proj_dir

Project directory created via proj_grid.

tasks

Positive integer: number of batchtools jobs, translated into one SLURM job array with this number of tasks.

hours

Hours of walltime to ask the SLURM scheduler.

gigabytes

Gigabytes of memory to ask the SLURM scheduler.

verbose

Logical: print messages?

cluster.functions

Cluster functions from batchtools, useful for testing.

Author

Toby Dylan Hocking

Details

This is Step 2 out of the typical 3 step pipeline (init grid, submit, read results).

Examples

N <- 80
library(data.table)
set.seed(1)
reg.dt <- data.table(
  x=runif(N, -2, 2),
  person=factor(rep(c("Alice","Bob"), each=0.5*N)))
reg.pattern.list <- list(
  easy=function(x, person)x^2,
  impossible=function(x, person)(x^2)*(-1)^as.integer(person))
SOAK <- mlr3resampling::ResamplingSameOtherSizesCV$new()
reg.task.list <- list()
for(pattern in names(reg.pattern.list)){
  f <- reg.pattern.list[[pattern]]
  task.dt <- data.table(reg.dt)[
  , y := f(x,person)+rnorm(N, sd=0.5)
  ][]
  task.obj <- mlr3::TaskRegr$new(
    pattern, task.dt, target="y")
  task.obj$col_roles$feature <- "x"
  task.obj$col_roles$stratum <- "person"
  task.obj$col_roles$subset <- "person"
  reg.task.list[[pattern]] <- task.obj
}
reg.learner.list <- list(
  featureless=mlr3::LearnerRegrFeatureless$new())
if(requireNamespace("rpart")){
  reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new()
}

pkg.proj.dir <- tempfile()
mlr3resampling::proj_grid(
  pkg.proj.dir,
  reg.task.list,
  reg.learner.list,
  SOAK,
  order_jobs = function(DT)1:2, # for CRAN.
  score_args=mlr3::msrs(c("regr.rmse", "regr.mae")))
mlr3resampling::proj_submit(pkg.proj.dir)
batchtools::waitForJobs()
fread(file.path(pkg.proj.dir, "results.csv"))