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Subclass for Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Calls adagio::pureCMAES() from package adagio.

Source

Hansen N (2016). “The CMA Evolution Strategy: A Tutorial.” 1604.00772.

Dictionary

This Tuner can be instantiated with the associated sugar function tnr():

tnr("cmaes")

Control Parameters

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").

Logging

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

Optimizer

This Tuner is based on bbotk::OptimizerCmaes which can be applied on any black box optimization problem. See also the documentation of bbotk.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

The gallery features a collection of case studies and demos about optimization.

  • Use the Hyperband optimizer with different budget parameters.

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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

# Hyperparameter Optimization

# load learner and set search space
learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE),
  minsplit = to_tune(p_dbl(2, 128, trafo = as.integer)),
  minbucket = to_tune(p_dbl(1, 64, trafo = as.integer))
)

# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
  tuner = tnr("cmaes"),
  task = tsk("penguins"),
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10)

# best performing hyperparameter configuration
instance$result
#>           cp minsplit minbucket learner_param_vals  x_domain classif.ce
#> 1: -6.755619        2  1.623453          <list[4]> <list[3]>  0.0173913

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp  minsplit minbucket classif.ce  x_domain_cp x_domain_minsplit
#>  1: -7.871627  66.71059 11.843669 0.03478261 0.0003814133                66
#>  2: -6.755619   2.00000  1.623453 0.01739130 0.0011643195                 2
#>  3: -2.302585 128.00000  1.000000 0.03478261 0.1000000000               128
#>  4: -3.421345 128.00000 16.450970 0.03478261 0.0326684756               128
#>  5: -9.210340  78.97977  1.000000 0.02608696 0.0001000000                78
#>  6: -9.210340  39.27754  1.000000 0.01739130 0.0001000000                39
#>  7: -5.299497   2.00000 23.805251 0.03478261 0.0049941030                 2
#>  8: -6.788993  84.96175  1.000000 0.02608696 0.0011261022                84
#>  9: -6.601750  52.27690  1.000000 0.02608696 0.0013579899                52
#> 10: -9.210340   2.00000  1.777742 0.01739130 0.0001000000                 2
#>     x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#>  1:                 11            0.006 2023-11-28 14:29:33        1        0
#>  2:                  1            0.007 2023-11-28 14:29:34        2        0
#>  3:                  1            0.007 2023-11-28 14:29:34        3        0
#>  4:                 16            0.007 2023-11-28 14:29:34        4        0
#>  5:                  1            0.006 2023-11-28 14:29:34        5        0
#>  6:                  1            0.005 2023-11-28 14:29:34        6        0
#>  7:                 23            0.007 2023-11-28 14:29:34        7        0
#>  8:                  1            0.006 2023-11-28 14:29:34        8        0
#>  9:                  1            0.005 2023-11-28 14:29:34        9        0
#> 10:                  1            0.007 2023-11-28 14:29:34       10        0
#>     errors      resample_result
#>  1:      0 <ResampleResult[21]>
#>  2:      0 <ResampleResult[21]>
#>  3:      0 <ResampleResult[21]>
#>  4:      0 <ResampleResult[21]>
#>  5:      0 <ResampleResult[21]>
#>  6:      0 <ResampleResult[21]>
#>  7:      0 <ResampleResult[21]>
#>  8:      0 <ResampleResult[21]>
#>  9:      0 <ResampleResult[21]>
#> 10:      0 <ResampleResult[21]>

# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(tsk("penguins"))