OptimizerDesignPoints class that implements optimization w.r.t. fixed design points. We simply search over a set of points fully specified by the user. The points in the design are evaluated in order as given.

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():

Super class

bbotk::Optimizer -> OptimizerDesignPoints

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Arguments

deep

Whether to make a deep clone.

Examples

library(data.table)
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))

design = data.table(x = c(0, 1))

optimizer = opt("design_points", design = design)

# Modifies the instance by reference
optimizer$optimize(instance) #> x x_domain y #> 1: 0 <list[1]> 0 # Returns best scoring evaluation instance$result
#>    x  x_domain y
#> 1: 0 <list[1]> 0
# Allows access of data.table of full path of all evaluations
as.data.table(instance\$archive)
#>    x y           timestamp batch_nr x_domain_x
#> 1: 0 0 2021-09-17 04:11:52        1          0
#> 2: 1 1 2021-09-17 04:11:52        2          1