mlr_optimizers_grid_search {bbotk} | R Documentation |
Optimization via Grid Search
Description
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, seeparadox::generate_design_grid()
.param_resolutions
named
integer()
Resolution per parameter, named by parameter ID, seeparadox::generate_design_grid()
.batch_size
integer(1)
Maximum number of points to try in a batch.
Progress Bars
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
Super classes
bbotk::Optimizer
-> bbotk::OptimizerBatch
-> OptimizerBatchGridSearch
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
OptimizerBatchGridSearch$new()
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)
# returns best scoring evaluation
instance$result
# allows access of data.table of full path of all evaluations
as.data.table(instance$archive$data)