OptimizerRandomSearch class that implements a simple Random Search.

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.

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

Dictionary

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

mlr_optimizers$get("random_search")
opt("random_search")

Parameters

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 -> OptimizerRandomSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerRandomSearch$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerRandomSearch$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("random_search") # modifies the instance by reference optimizer$optimize(instance)
#> x x_domain y #> 1: 0.2168705 <list[1]> 0.04703283
# returns best scoring evaluation instance$result
#> x x_domain y #> 1: 0.2168705 <list[1]> 0.04703283
# 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: 0.5150116 0.26523692 <list[1]> 2021-09-17 04:11:58 1 #> 2: -0.4124651 0.17012744 <list[1]> 2021-09-17 04:11:58 2 #> 3: -0.2510609 0.06303155 <list[1]> 2021-09-17 04:11:58 3 #> 4: 0.4571093 0.20894896 <list[1]> 2021-09-17 04:11:58 4 #> 5: 0.7587425 0.57569024 <list[1]> 2021-09-17 04:11:58 5 #> 6: 0.2168705 0.04703283 <list[1]> 2021-09-17 04:11:58 6 #> 7: 0.3304732 0.10921252 <list[1]> 2021-09-17 04:11:58 7 #> 8: 0.2977294 0.08864280 <list[1]> 2021-09-17 04:11:58 8 #> 9: -0.7059954 0.49842951 <list[1]> 2021-09-17 04:11:58 9 #> 10: 0.2469404 0.06097955 <list[1]> 2021-09-17 04:11:58 10