add OptimizerIrace

[be-marc]

What's new?

• adds OptimizerIrace
• instances are passed via the already implemented constants field / parameters
• the user implements an objective function with xs and instances (see example)
• all Objective subclasses are usable
• race and step are recorded in archive
• improved documentation

Example

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))

# sample noise values to 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)