Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Strategy

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

• book section on tuners.

• mlr3hyperband extension package for the Hyperband algorithm.

See also

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

# 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(
  method = 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: -5.007393      128         1          <list[4]> <list[3]> 0.03478261

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp  minsplit minbucket classif.ce x_domain_cp x_domain_minsplit
#>  1: -2.302585  14.22901 64.000000 0.13043478 0.100000000                14
#>  2: -5.007393 128.00000  1.000000 0.03478261 0.006688315               128
#>  3: -5.843637 128.00000  1.000000 0.03478261 0.002898284               128
#>  4: -9.210340 128.00000 45.021667 0.05217391 0.000100000               128
#>  5: -4.837406 128.00000  1.000000 0.03478261 0.007927593               128
#>  6: -2.302585 125.27374  1.000000 0.03478261 0.100000000               125
#>  7: -5.378950 115.39845  8.615102 0.03478261 0.004612662               115
#>  8: -6.268155 128.00000  1.000000 0.03478261 0.001895722               128
#>  9: -4.461240 128.00000  1.000000 0.03478261 0.011548031               128
#> 10: -5.307489 124.58107  8.309251 0.03478261 0.004954353               124
#>     x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#>  1:                 64            0.008 2022-11-27 11:18:55        1        0
#>  2:                  1            0.008 2022-11-27 11:18:55        2        0
#>  3:                  1            0.008 2022-11-27 11:18:55        3        0
#>  4:                 45            0.007 2022-11-27 11:18:55        4        0
#>  5:                  1            0.008 2022-11-27 11:18:55        5        0
#>  6:                  1            0.009 2022-11-27 11:18:55        6        0
#>  7:                  8            0.009 2022-11-27 11:18:55        7        0
#>  8:                  1            0.008 2022-11-27 11:18:55        8        0
#>  9:                  1            0.008 2022-11-27 11:18:55        9        0
#> 10:                  8            0.008 2022-11-27 11:18:55       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"))