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

Source

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

Dictionary

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

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

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").

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.

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

library(data.table)

# retrieve task
task = tsk("pima")

# 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))
)

# hyperparameter tuning on the pima indians diabetes data set
instance = tune(
  method = "cmaes",
  task = task,
  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: -7.510346 72.06259  14.34832          <list[4]> <list[3]>  0.2851562

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp   minsplit minbucket classif.ce  x_domain_cp x_domain_minsplit
#>  1: -6.949432  69.031796  1.000000  0.2890625 0.0009591793                69
#>  2: -7.510346  72.062593 14.348322  0.2851562 0.0005473919                72
#>  3: -2.302585 128.000000  1.000000  0.3203125 0.1000000000               128
#>  4: -4.931336  86.050476  5.029061  0.2890625 0.0072168542                86
#>  5: -9.210340 128.000000  1.000000  0.3242188 0.0001000000               128
#>  6: -3.494726 119.609550 25.662615  0.2851562 0.0303570703               119
#>  7: -6.247934   4.874612  1.000000  0.2890625 0.0019344467                 4
#>  8: -3.657932  53.990282 44.564985  0.2851562 0.0257857872                53
#>  9: -2.302585  78.557521 64.000000  0.3203125 0.1000000000                78
#> 10: -4.201757 125.018418  1.000000  0.3203125 0.0149692586               125
#>     x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#>  1:                  1            0.015 2022-05-24 04:26:36        1        0
#>  2:                 14            0.014 2022-05-24 04:26:36        2        0
#>  3:                  1            0.014 2022-05-24 04:26:37        3        0
#>  4:                  5            0.015 2022-05-24 04:26:37        4        0
#>  5:                  1            0.014 2022-05-24 04:26:37        5        0
#>  6:                 25            0.014 2022-05-24 04:26:37        6        0
#>  7:                  1            0.017 2022-05-24 04:26:37        7        0
#>  8:                 44            0.014 2022-05-24 04:26:37        8        0
#>  9:                 64            0.014 2022-05-24 04:26:37        9        0
#> 10:                  1            0.015 2022-05-24 04:26:37       10        0
#>     errors      resample_result
#>  1:      0 <ResampleResult[22]>
#>  2:      0 <ResampleResult[22]>
#>  3:      0 <ResampleResult[22]>
#>  4:      0 <ResampleResult[22]>
#>  5:      0 <ResampleResult[22]>
#>  6:      0 <ResampleResult[22]>
#>  7:      0 <ResampleResult[22]>
#>  8:      0 <ResampleResult[22]>
#>  9:      0 <ResampleResult[22]>
#> 10:      0 <ResampleResult[22]>

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