Create control object for hyperparameter tuning with Irace.

Tuning with iterated F-Racing with method irace::irace. All kinds of parameter types can be handled. We return the best of the final elite candidates found by irace in the last race. Its estimated performance is the mean of all evaluations ever done for that candidate. More information on irace can be found in package vignette: vignette("irace-package", package = "irace")

For resampling you have to pass a ResampleDesc, not a ResampleInstance. The resampling strategy is randomly instantiated n.instances times and these are the instances in the sense of irace (instances element of tunerConfig in irace::irace). Also note that irace will always store its tuning results in a file on disk, see the package documentation for details on this and how to change the file path.

Usage

makeTuneControlIrace(
  impute.val = NULL,
  n.instances = 100L,
  show.irace.output = FALSE,
  tune.threshold = FALSE,
  tune.threshold.args = list(),
  log.fun = "default",
  final.dw.perc = NULL,
  budget = NULL,
  ...
)

Arguments

impute.val

(numeric)
If something goes wrong during optimization (e.g. the learner crashes), this value is fed back to the tuner, so the tuning algorithm does not abort. Imputation is only active if on.learner.error is configured not to stop in configureMlr. It is not stored in the optimization path, an NA and a corresponding error message are logged instead. Note that this value is later multiplied by -1 for maximization measures internally, so you need to enter a larger positive value for maximization here as well. Default is the worst obtainable value of the performance measure you optimize for when you aggregate by mean value, or Inf instead. For multi-criteria optimization pass a vector of imputation values, one for each of your measures, in the same order as your measures.

n.instances

(integer(1))
Number of random resampling instances for irace, see details. Default is 100.

show.irace.output

(logical(1))
Show console output of irace while tuning? Default is FALSE.

tune.threshold

(logical(1))
Should the threshold be tuned for the measure at hand, after each hyperparameter evaluation, via tuneThreshold? Only works for classification if the predict type is “prob”. Default is FALSE.

tune.threshold.args

(list)
Further arguments for threshold tuning that are passed down to tuneThreshold. Default is none.

log.fun

(function | character(1))
Function used for logging. If set to “default” (the default), the evaluated design points, the resulting performances, and the runtime will be reported. If set to “memory” the memory usage for each evaluation will also be displayed, with character(1) small increase in run time. Otherwise character(1) function with arguments learner, resampling, measures, par.set, control, opt.path, dob, x, y, remove.nas, stage and prev.stage is expected. The default displays the performance measures, the time needed for evaluating, the currently used memory and the max memory ever used before (the latter two both taken from gc). See the implementation for details.

final.dw.perc

(boolean)
If a Learner wrapped by a makeDownsampleWrapper is used, you can define the value of dw.perc which is used to train the Learner with the final parameter setting found by the tuning. Default is NULL which will not change anything.

budget

(integer(1))
Maximum budget for tuning. This value restricts the number of function evaluations. It is passed to maxExperiments.

...

(any)
Further control parameters passed to the control arguments of cmaes::cma_es or GenSA::GenSA, as well as towards the tunerConfig argument of irace::irace.

Value

(TuneControlIrace)

See also

Other tune: TuneControl, getNestedTuneResultsOptPathDf(), getNestedTuneResultsX(), getResamplingIndices(), getTuneResult(), makeModelMultiplexerParamSet(), makeModelMultiplexer(), makeTuneControlCMAES(), makeTuneControlDesign(), makeTuneControlGenSA(), makeTuneControlGrid(), makeTuneControlMBO(), makeTuneControlRandom(), makeTuneWrapper(), tuneParams(), tuneThreshold()