Optimization via Iterated Racing

Description

OptimizerIrace 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 OptimizerIrace.

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 OptimizerIrace. 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 class

bbotk::Optimizer -> OptimizerIrace

Methods

Public methods

• OptimizerIrace$new()

• OptimizerIrace$clone()

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerIrace$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerIrace$clone(deep = FALSE)

Arguments

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) {
  a = 1
  b = 5.1 / (4 * (pi ^ 2))
  c = 5 / pi
  r = 6
  s = 10
  t = 1 / (8 * pi)

  data.table(y = (
    a * ((xdt[["x2"]] - 
    b * (xdt[["x1"]] ^ 2L) + 
    c * xdt[["x1"]] - r) ^ 2) + 
    ((s * (1 - t)) * cos(xdt[["x1"]])) + 
    unlist(instances)))
}

objective = ObjectiveRFunDt$new(fun = fun, domain = domain,  codomain = codomain)

instance = OptimInstanceSingleCrit$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)