mlr_optimizers_irace {bbotk}

Optimization via Iterated Racing

Description

OptimizerBatchIrace class that implements iterated racing. Calls irace::irace() from package irace.

Parameters

instances

list()
A list of instances where the configurations executed on.

targetRunnerParallel

⁠function()⁠
A function that executes the objective function with a specific parameter configuration and instance. A default function is provided, see section "Target Runner and Instances".

For the meaning of all other parameters, see irace::defaultScenario(). Note that we have removed all control parameters which refer to the termination of the algorithm. Use TerminatorEvals instead. Other terminators do not work with OptimizerBatchIrace.

In contrast to irace::defaultScenario(), we set digits = 15. This represents double parameters with a higher precision and avoids rounding errors.

Target Runner and Instances

The irace package uses a targetRunner script or R function to evaluate a configuration on a particular instance. Usually it is not necessary to specify a targetRunner function when using OptimizerBatchIrace. A default function is used that forwards several configurations and instances to the user defined objective function. As usually, the user defined function has a xs, xss or xdt parameter depending on the used Objective class. For irace, the function needs an additional instances parameter.

fun = function(xs, instances) {
 # function to evaluate configuration in `xs` on instance `instances`
}

Archive

The Archive holds the following additional columns:

• "race" (integer(1))
  Race iteration.

• "step" (integer(1))
  Step number of race.

• "instance" (integer(1))
  Identifies instances across races and steps.

• "configuration" (integer(1))
  Identifies configurations across races and steps.

Result

The optimization result (instance$result) is the best performing elite of the final race. The reported performance is the average performance estimated on all used instances.

Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function opt():

mlr_optimizers$get("irace")
opt("irace")

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 -> OptimizerBatchIrace

Methods

Public methods

• OptimizerBatchIrace$new()

• OptimizerBatchIrace$clone()

Inherited methods

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

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchIrace$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchIrace$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Source

Lopez-Ibanez M, Dubois-Lacoste J, Caceres LP, Birattari M, Stuetzle T (2016). “The irace package: Iterated racing for automatic algorithm configuration.” Operations Research Perspectives, 3, 43–58. doi:10.1016/j.orp.2016.09.002.

Examples

library(data.table)

search_space = domain = ps(
  x1 = p_dbl(-5, 10),
  x2 = p_dbl(0, 15)
)

codomain = ps(y = p_dbl(tags = "minimize"))

# branin function with noise
# the noise generates different instances of the branin function
# the noise values are passed via the `instances` parameter
fun = function(xdt, instances) {
  ys = branin(xdt[["x1"]], xdt[["x2"]], noise = as.numeric(instances))
  data.table(y = ys)
}

# define objective with instances as a constant
objective = ObjectiveRFunDt$new(
 fun = fun,
 domain = domain,
 codomain = codomain,
 constants = ps(instances = p_uty()))

instance = OptimInstanceBatchSingleCrit$new(
  objective = objective,
  search_space = search_space,
  terminator = trm("evals", n_evals = 1000))

# create instances of branin function
instances = rnorm(10, mean = 0, sd = 0.1)

# load optimizer irace and set branin instances
optimizer = opt("irace", instances = instances)

# modifies the instance by reference
optimizer$optimize(instance)

# best scoring configuration
instance$result

# all evaluations
as.data.table(instance$archive)