Optimization via Grid Search

OptimizerBatchGridSearch 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 classes

bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchGridSearch

Methods

Public methods

• OptimizerBatchGridSearch$new()

• OptimizerBatchGridSearch$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()
• bbotk::OptimizerBatch$optimize()

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Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchGridSearch$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchGridSearch$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 = OptimInstanceBatchSingleCrit$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
#>        <num>    <list>      <num>
#> 1: 0.1111111 <list[1]> 0.01234568

# returns best scoring evaluation
instance$result
#>            x  x_domain          y
#>        <num>    <list>      <num>
#> 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
#>          <num>      <num>    <list>              <POSc>    <int>
#>  1:  0.1111111 0.01234568 <list[1]> 2024-11-27 19:25:21        1
#>  2: -0.3333333 0.11111111 <list[1]> 2024-11-27 19:25:21        2
#>  3: -0.7777778 0.60493827 <list[1]> 2024-11-27 19:25:21        3
#>  4:  0.7777778 0.60493827 <list[1]> 2024-11-27 19:25:21        4
#>  5:  0.5555556 0.30864198 <list[1]> 2024-11-27 19:25:21        5
#>  6:  0.3333333 0.11111111 <list[1]> 2024-11-27 19:25:21        6
#>  7: -1.0000000 1.00000000 <list[1]> 2024-11-27 19:25:21        7
#>  8:  1.0000000 1.00000000 <list[1]> 2024-11-27 19:25:21        8
#>  9: -0.1111111 0.01234568 <list[1]> 2024-11-27 19:25:21        9
#> 10: -0.5555556 0.30864198 <list[1]> 2024-11-27 19:25:21       10