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: -9.21034 35.35649  34.25562          <list[4]> <list[3]> 0.04347826

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp   minsplit minbucket classif.ce x_domain_cp x_domain_minsplit
#>  1: -2.302585 108.112062 64.000000 0.10434783 0.100000000               108
#>  2: -9.210340  35.356493 34.255616 0.04347826 0.000100000                35
#>  3: -2.302585   9.697157 46.044377 0.04347826 0.100000000                 9
#>  4: -2.302585  25.401639 63.205350 0.10434783 0.100000000                25
#>  5: -2.302585 128.000000 12.347091 0.04347826 0.100000000               128
#>  6: -4.896475 128.000000 64.000000 0.10434783 0.007472875               128
#>  7: -4.746180 122.589603 64.000000 0.10434783 0.008684809               122
#>  8: -9.210340   2.000000  8.692479 0.04347826 0.000100000                 2
#>  9: -2.302585  15.495505 64.000000 0.10434783 0.100000000                15
#> 10: -6.566245  79.230407 25.393594 0.04347826 0.001407071                79
#>     x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#>  1:                 64            0.012 2022-12-22 07:40:12        1        0
#>  2:                 34            0.011 2022-12-22 07:40:12        2        0
#>  3:                 46            0.011 2022-12-22 07:40:12        3        0
#>  4:                 63            0.011 2022-12-22 07:40:12        4        0
#>  5:                 12            0.012 2022-12-22 07:40:13        5        0
#>  6:                 64            0.013 2022-12-22 07:40:13        6        0
#>  7:                 64            0.012 2022-12-22 07:40:13        7        0
#>  8:                  8            0.011 2022-12-22 07:40:13        8        0
#>  9:                 64            0.012 2022-12-22 07:40:13        9        0
#> 10:                 25            0.012 2022-12-22 07:40:13       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"))