mlr3resampling (version 2025.6.23)

proj_results: Combine and save results in a project

Description

proj_results globs the RDS result files in the project directory, and combines them into a result table via rbindlist(). proj_results_save saves that result table to results.rds and results.csv.

Usage

proj_results(proj_dir)
proj_results_save(proj_dir)

Value

proj_results returns a data table with all columns, whereas

proj_results_save returns the same table with only atomic columns.

Arguments

proj_dir

Project directory created via proj_grid.

Author

Toby Dylan Hocking

Details

This is Step 3 out of the typical 3 step pipeline (init grid, submit, read results). Actually, if step 2 worked as intended, the last proj_compute calls proj_results_save, which saves up to three result files to disk that you can read directly:

results.csv

contains test measures for each split, and can be read via fread()

results.rds

contains additional list columns for learner and pred (useful for model interpretation), and can be read via readRDS()

learners.csv

only exists if learner column is a data frame, in which case it contains the atomic columns, along with meta-data describing each split.

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_until_done(pkg.proj.dir)
fread(file.path(pkg.proj.dir, "results.csv"))