Optimization via Grid Search

Source: R/OptimizerGridSearch.R

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