OptimizerGridSearch class that implements grid search. 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.

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.

Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function opt():

mlr_optimizers$get("grid_search")
opt("grid_search")

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.

Progress Bars

$optimize() supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

Super class

bbotk::Optimizer -> OptimizerGridSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerGridSearch$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerGridSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

search_space = domain = ps(x = p_dbl(lower = -1, upper = 1)) codomain = ps(y = p_dbl(tags = "minimize")) objective_function = function(xs) { list(y = as.numeric(xs)^2) } objective = ObjectiveRFun$new( fun = objective_function, domain = domain, codomain = codomain) instance = OptimInstanceSingleCrit$new( objective = objective, search_space = search_space, terminator = trm("evals", n_evals = 10)) optimizer = opt("grid_search") # modifies the instance by reference optimizer$optimize(instance)
#> x x_domain y #> 1: 0.1111111 <list[1]> 0.01234568
# returns best scoring evaluation instance$result
#> x x_domain y #> 1: 0.1111111 <list[1]> 0.01234568
# allows access of data.table of full path of all evaluations as.data.table(instance$archive$data)
#> x y x_domain timestamp batch_nr #> 1: 1.0000000 1.00000000 <list[1]> 2021-09-17 04:11:53 1 #> 2: 0.1111111 0.01234568 <list[1]> 2021-09-17 04:11:53 2 #> 3: -0.3333333 0.11111111 <list[1]> 2021-09-17 04:11:53 3 #> 4: 0.5555556 0.30864198 <list[1]> 2021-09-17 04:11:53 4 #> 5: -0.5555556 0.30864198 <list[1]> 2021-09-17 04:11:53 5 #> 6: -1.0000000 1.00000000 <list[1]> 2021-09-17 04:11:53 6 #> 7: 0.3333333 0.11111111 <list[1]> 2021-09-17 04:11:53 7 #> 8: -0.1111111 0.01234568 <list[1]> 2021-09-17 04:11:53 8 #> 9: -0.7777778 0.60493827 <list[1]> 2021-09-17 04:11:53 9 #> 10: 0.7777778 0.60493827 <list[1]> 2021-09-17 04:11:53 10