Optimization via Grid Search
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.
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")resolutioninteger(1)
Resolution of the grid, see paradox::generate_design_grid().
param_resolutionsnamed integer()
Resolution per parameter, named by parameter ID, see
paradox::generate_design_grid().
batch_sizeinteger(1)
Maximum number of points to try in a batch.
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
bbotk::Optimizer -> OptimizerGridSearch
new()
Creates a new instance of this R6 class.
OptimizerGridSearch$new()
clone()
The objects of this class are cloneable with this method.
OptimizerGridSearch$clone(deep = FALSE)
deepWhether to make a deep clone.
library(paradox)
domain = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))
search_space = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))
codomain = ParamSet$new(list(ParamDbl$new("y", tags = "minimize")))
objective_function = function(xs) {
list(y = as.numeric(xs)^2)
}
objective = ObjectiveRFun$new(fun = objective_function,
domain = domain,
codomain = codomain)
terminator = trm("evals", n_evals = 10)
instance = OptimInstanceSingleCrit$new(
objective = objective,
search_space = search_space,
terminator = terminator)
optimizer = opt("grid_search")
# Modifies the instance by reference
optimizer$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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.