Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Strategy

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.

See also

Package mlr3hyperband for hyperband tuning.

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

Super classes

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

Methods

Public methods

• TunerCmaes$new()

• TunerCmaes$clone()

Inherited methods

• mlr3tuning::Tuner$format()
• mlr3tuning::Tuner$help()
• 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(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: -2.596505      128  54.21241          <list[4]> <list[3]>       0.25

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp  minsplit minbucket classif.ce x_domain_cp x_domain_minsplit
#>  1: -5.597545  45.86625  1.000000  0.2851562 0.003706954                45
#>  2: -2.302585  51.49676  1.000000  0.2539062 0.100000000                51
#>  3: -2.302585  98.68626  1.000000  0.2539062 0.100000000                98
#>  4: -4.837910 102.77424  9.204627  0.2539062 0.007923597               102
#>  5: -6.145764  26.26974 53.855930  0.2539062 0.002142538                26
#>  6: -2.596505 128.00000 54.212410  0.2500000 0.074533585               128
#>  7: -6.820869   2.00000 19.848495  0.2695312 0.001090773                 2
#>  8: -3.703357   2.00000 15.904776  0.2851562 0.024640672                 2
#>  9: -5.568117  65.57053 50.586336  0.2539062 0.003817661                65
#> 10: -2.302585  58.96777 29.683668  0.2539062 0.100000000                58
#>     x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#>  1:                  1            0.015 2022-08-25 11:28:15        1        0
#>  2:                  1            0.013 2022-08-25 11:28:15        2        0
#>  3:                  1            0.013 2022-08-25 11:28:15        3        0
#>  4:                  9            0.014 2022-08-25 11:28:15        4        0
#>  5:                 53            0.048 2022-08-25 11:28:15        5        0
#>  6:                 54            0.014 2022-08-25 11:28:15        6        0
#>  7:                 19            0.015 2022-08-25 11:28:15        7        0
#>  8:                 15            0.013 2022-08-25 11:28:15        8        0
#>  9:                 50            0.015 2022-08-25 11:28:15        9        0
#> 10:                 29            0.013 2022-08-25 11:28:15       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(task)