bbotk (version 0.2.2)

mlr_optimizers_grid_search: Optimization via Grid Search

Description

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.

Super class

bbotk::Optimizer -> OptimizerGridSearch

Methods

Public methods

• OptimizerGridSearch$new()

• OptimizerGridSearch$clone()

• bbotk::Optimizer$format()

• bbotk::Optimizer$optimize()

• bbotk::Optimizer$print()

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

# NOT RUN {
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
instance$archive$data()
# }