proj_compute: Compute resampling results in a project

In mlr3resampling: Resampling Algorithms for 'mlr3' Framework

Compute resampling results in a project

Description

proj_compute() looks in grid_jobs.csv for a row with status=="not started", and picks the first one to work on, updating status="started". After having run train() and predict(), a data table with one row is saved to an RDS file in the grid_jobs directory, and status="done" is updated. proj_compute_until_done() keeps doing that in a while loop.

Usage

proj_compute(proj_dir, verbose = FALSE)
proj_compute_until_done(proj_dir, verbose = FALSE)

Arguments

proj_dir	Project directory created by proj_grid.
verbose	Logical: print messages?

Details

If everything goes well, the user should not need to run this function. Instead, the user runs proj_submit as Step 2 out of the typical 3 step pipeline (init grid, submit, read results). proj_compute can sometimes be useful for testing or debugging the submit step, since it runs one split at a time.

Value

Data table with one row of results.

Author(s)

Toby Dylan Hocking

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_compute(pkg.proj.dir)
fread(file.path(pkg.proj.dir, "grid_jobs.csv"))
mlr3resampling::proj_compute_until_done(pkg.proj.dir)
fread(file.path(pkg.proj.dir, "grid_jobs.csv"))

mlr3resampling documentation built on June 23, 2025, 5:08 p.m.