`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.

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

:

mlr_optimizers$get("design_points") opt("design_points")

`batch_size`

`integer(1)`

Maximum number of configurations to try in a batch.`design`

data.table::data.table

Design points to try in search, one per row.

`$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")`

.

`bbotk::Optimizer`

-> `OptimizerDesignPoints`

`new()`

Creates a new instance of this R6 class.

OptimizerDesignPoints$new()

`clone()`

The objects of this class are cloneable with this method.

OptimizerDesignPoints$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

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#> 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