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Generate a Benchmark Grid Design

Source: R/benchmark_grid.R
benchmark_grid.Rd

Takes a lists of Task, a list of Learner and a list of Resampling to generate a design in an expand.grid() fashion (a.k.a. cross join or Cartesian product).

There are two modes of operation, depending on the flag paired.

  • With paired set to FALSE (default), resampling strategies are not allowed to be instantiated, and instead will be instantiated per task internally. The only exception to this rule applies if all tasks have exactly the same number of rows, and the resamplings are all instantiated for such tasks. The grid will be generated based on the Cartesian product of tasks, learners, and resamplings. Because the resamplings are instantiated on the tasks, reproducibility requires a seed to be set before calling this function, as this process is stochastic.

  • With paired set to TRUE, tasks and resamplings are treated as pairs. I.e., you must provide as many tasks as corresponding instantiated resamplings. The grid will be generated based on the Cartesian product of learners and pairs.

Usage

benchmark_grid(
  tasks,
  learners,
  resamplings,
  param_values = NULL,
  paired = FALSE
)

Arguments

tasks

(list of Task).

learners

(list of Learner).

resamplings

(list of Resampling).

param_values

(list())
If you want to try many parameter settings for learners, you can pass them through the design which is optimized to be faster than creating learners for each setting.

A list of lists of named lists, from outer to inner:

  1. One list element for each Learner.

  2. One list element for each hyperparameter configuration to try.

  3. Named list of hyperparameter settings to set in the Learner, possibly overwriting already set set hyperparameters in the Learner.

paired

(logical(1))
Set this to TRUE if the resamplings are instantiated on the tasks, i.e., the tasks and resamplings are paired. You need to provide the same number of tasks and instantiated resamplings.

Value

(data.table::data.table()) with the cross product of the input vectors.

See also

Other benchmark: BenchmarkResult, benchmark()

Examples

tasks = list(tsk("penguins"), tsk("sonar"))
learners = list(lrn("classif.featureless"), lrn("classif.rpart"))
resamplings = list(rsmp("cv"), rsmp("subsampling"))

# Set a seed to ensure reproducibility of the resampling instantiation
set.seed(123)
grid = benchmark_grid(tasks, learners, resamplings)
# the resamplings are now instantiated
head(grid$resampling[[1]]$instance)
#> Key: <fold>
#>    row_id  fold
#>     <int> <int>
#> 1:     12     1
#> 2:     22     1
#> 3:     24     1
#> 4:     42     1
#> 5:     47     1
#> 6:     69     1
print(grid)
#>        task             learner  resampling
#>      <char>              <char>      <char>
#> 1: penguins classif.featureless          cv
#> 2: penguins classif.featureless subsampling
#> 3: penguins       classif.rpart          cv
#> 4: penguins       classif.rpart subsampling
#> 5:    sonar classif.featureless          cv
#> 6:    sonar classif.featureless subsampling
#> 7:    sonar       classif.rpart          cv
#> 8:    sonar       classif.rpart subsampling
if (FALSE) {
benchmark(grid)
}

# paired
learner = lrn("classif.rpart")
task1 = tsk("penguins")
task2 = tsk("german_credit")
res1 = rsmp("holdout")
res2 = rsmp("holdout")
res1$instantiate(task1)
res2$instantiate(task2)
design = benchmark_grid(list(task1, task2), learner, list(res1, res2), paired = TRUE)
print(design)
#>             task       learner resampling
#>           <char>        <char>     <char>
#> 1:      penguins classif.rpart    holdout
#> 2: german_credit classif.rpart    holdout

# manual construction of the grid with data.table::CJ()
grid = data.table::CJ(task = tasks, learner = learners,
  resampling = resamplings, sorted = FALSE)

# manual instantiation (not suited for a fair comparison of learners!)
Map(function(task, resampling) {
  resampling$instantiate(task)
}, task = grid$task, resampling = grid$resampling)
#> [[1]]
#> <ResamplingCV>: Cross-Validation
#> * Iterations: 10
#> * Instantiated: TRUE
#> * Parameters: folds=10
#> 
#> [[2]]
#> <ResamplingSubsampling>: Subsampling
#> * Iterations: 30
#> * Instantiated: TRUE
#> * Parameters: repeats=30, ratio=0.6667
#> 
#> [[3]]
#> <ResamplingCV>: Cross-Validation
#> * Iterations: 10
#> * Instantiated: TRUE
#> * Parameters: folds=10
#> 
#> [[4]]
#> <ResamplingSubsampling>: Subsampling
#> * Iterations: 30
#> * Instantiated: TRUE
#> * Parameters: repeats=30, ratio=0.6667
#> 
#> [[5]]
#> <ResamplingCV>: Cross-Validation
#> * Iterations: 10
#> * Instantiated: TRUE
#> * Parameters: folds=10
#> 
#> [[6]]
#> <ResamplingSubsampling>: Subsampling
#> * Iterations: 30
#> * Instantiated: TRUE
#> * Parameters: repeats=30, ratio=0.6667
#> 
#> [[7]]
#> <ResamplingCV>: Cross-Validation
#> * Iterations: 10
#> * Instantiated: TRUE
#> * Parameters: folds=10
#> 
#> [[8]]
#> <ResamplingSubsampling>: Subsampling
#> * Iterations: 30
#> * Instantiated: TRUE
#> * Parameters: repeats=30, ratio=0.6667
#> 
if (FALSE) {
benchmark(grid)
}