benchmark_grid: Generate a Benchmark Grid Design

In mlr3: Machine Learning in R - Next Generation

Generate a Benchmark Grid Design

Description

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: One list element for each Learner. One list element for each hyperparameter configuration to try. 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

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-benchmarking

• Package mlr3viz for some generic visualizations.

• mlr3benchmark for post-hoc analysis of benchmark results.

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)
print(grid)
## Not run: 
benchmark(grid)

## End(Not run)

# 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)

# 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)
## Not run: 
benchmark(grid)

## End(Not run)

mlr3 documentation built on Nov. 17, 2023, 5:07 p.m.