Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Strategy

Subclass that implements CMA-ES calling adagio::pureCMAES() from package adagio.

Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

mlr_tuners$get("cmaes")
tnr("cmaes")

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Parameters

sigma

numeric(1)

start_values

character(1)
Create random start values or based on center of search space? In the latter case, it is the center of the parameters before a trafo is applied.

For the meaning of the control parameters, see adagio::pureCMAES(). Note that we have removed all control parameters which refer to the termination of the algorithm and where our terminators allow to obtain the same behavior.

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").

See also

Package mlr3hyperband for hyperband tuning.

Other Tuner: mlr_tuners_design_points, mlr_tuners_gensa, mlr_tuners_grid_search, mlr_tuners_nloptr, mlr_tuners_random_search

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerCmaes

Methods

Public methods

• TunerCmaes$new()

• TunerCmaes$clone()

Inherited methods

• mlr3tuning::Tuner$format()

• mlr3tuning::Tuner$print()

• mlr3tuning::TunerFromOptimizer$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

TunerCmaes$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerCmaes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
library(paradox)
library(data.table)
search_space = ParamSet$new(list(
  ParamDbl$new("cp", lower = 0.001, upper = 0.1)
))
terminator = trm("evals", n_evals = 10)
instance = TuningInstanceSingleCrit$new(
  task = tsk("iris"),
  learner = lrn("classif.rpart"),
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  search_space = search_space,
  terminator = terminator
)
tt = tnr("cmaes")
# modifies the instance by reference
tt$optimize(instance)
#> Error in B %*% diag(D^-1): non-conformable arguments
# returns best configuration and best performance
instance$result
#> NULL
# allows access of data.table of full path of all evaluations
instance$archive
#> <ArchiveTuning>
#>       cp classif.ce           timestamp batch_nr
#> 1: 0.050       0.08 2021-03-15 04:41:45        1
#> 2: 0.100       0.08 2021-03-15 04:41:45        2
#> 3: 0.100       0.08 2021-03-15 04:41:45        3
#> 4: 0.100       0.08 2021-03-15 04:41:45        4
#> 5: 0.001       0.08 2021-03-15 04:41:45        5
#> 6: 0.094       0.08 2021-03-15 04:41:45        6
#> 7: 0.100       0.08 2021-03-15 04:41:45        7
#> 8: 0.100       0.08 2021-03-15 04:41:45        8