proj_results: Combine and save results in a project

In mlr3resampling: Resampling Algorithms for 'mlr3' Framework

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 one or more CSV files (redundant with RDS data, but more convenient). proj_fread reads the CSV files, adding columns from results.csv to learners*.csv.

Usage

proj_results(proj_dir, verbose=FALSE)
proj_results_save(proj_dir, verbose=FALSE)
proj_fread(proj_dir)

Arguments

proj_dir	Project directory created via proj_grid.
verbose	logical: cat progress? (default FALSE)

Details

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

results.csv

contains test measures for each split.

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.

learners_*.csv

only exists if learner column is a named list of data frames: star in file name is expanded using list names, with CSV data taken from atomic columns.

Value

proj_results returns a data table with all columns, whereas proj_results_save returns the same table with only atomic columns. proj_fread returns a list with names corresponding to CSV files in the test directory, and values are the data tables that result from fread.

Author(s)

Toby Dylan Hocking

Examples

N <- 80
library(data.table)
set.seed(1)
reg.dt <- data.table(
  x=runif(N, -2, 2),
  noise=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 <- c("x","noise")
  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,
  save_learner=function(L){
    if(inherits(L, "LearnerRegrRpart")){
      list(rpart=L$model$frame)
    }
  },
  order_jobs = function(DT)which(DT$iteration==1)[1:2], # for CRAN.
  score_args=mlr3::msrs(c("regr.rmse", "regr.mae")))
computed <- mlr3resampling::proj_compute_all(pkg.proj.dir)
result_list <- mlr3resampling::proj_fread(pkg.proj.dir)
result_list$learners_rpart.csv # one row per node in decision tree.
result_list$results.csv # test error in regr.* columns.

mlr3resampling documentation built on Nov. 21, 2025, 1:07 a.m.