Subclass for grid search tuning.

The grid is constructed as a Cartesian product over discretized values per parameter, see paradox::generate_design_grid(). The points of the grid are evaluated in a random order.

Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

mlr_tuners$get("grid_search")
tnr("grid_search")

Parallelization

In order to support general termination criteria and parallelization, we evaluate points in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria. A batch contains of batch_size times resampling$iters jobs. E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can utilize up to 50 cores.

Parallelization is supported via package future (see mlr3::benchmark()'s section on parallelization for more details).

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Parameters

resolution

integer(1)
Resolution of the grid, see paradox::generate_design_grid().

param_resolutions

named integer()
Resolution per parameter, named by parameter ID, see paradox::generate_design_grid().

batch_size

integer(1)
Maximum number of points to try in a batch.

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerGridSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

TunerGridSearch$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

TunerGridSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3) library(paradox) search_space = ParamSet$new(list( ParamDbl$new("cp", lower = 0.001, upper = 0.1) )) terminator = trm("evals", n_evals = 3) instance = TuningInstanceSingleCrit$new( task = tsk("iris"), learner = lrn("classif.rpart"), resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = search_space, terminator = terminator ) tt = tnr("grid_search") # modifies the instance by reference tt$optimize(instance)
#> cp learner_param_vals x_domain classif.ce #> 1: 0.067 <list[2]> <list[1]> 0.08
# returns best configuration and best performance instance$result
#> cp learner_param_vals x_domain classif.ce #> 1: 0.067 <list[2]> <list[1]> 0.08
# allows access of data.table of full path of all evaluations instance$archive
#> <ArchiveTuning> #> cp classif.ce uhash x_domain #> 1: 0.067 0.08 1e46c2b8-b701-4f2e-83a5-85dc1f53475f <list[1]> #> 2: 0.089 0.08 e4824abc-bcf9-42a7-bdc7-d70fb57a765a <list[1]> #> 3: 0.034 0.08 6fd26b0a-1b94-4c1a-86e6-49f7ced18d97 <list[1]> #> timestamp batch_nr #> 1: 2020-09-28 04:30:43 1 #> 2: 2020-09-28 04:30:43 2 #> 3: 2020-09-28 04:30:43 3