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: -5.712982      128  48.47419          <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: -8.572224 128.00000   1.00000 0.06086957 0.0001892912               128
#>  2: -2.302585 128.00000   1.00000 0.06086957 0.1000000000               128
#>  3: -6.626493 110.66894  64.00000 0.11304348 0.0013248014               110
#>  4: -2.302585 128.00000  60.76501 0.11304348 0.1000000000               128
#>  5: -2.529781 128.00000  54.74229 0.05217391 0.0796764822               128
#>  6: -6.188277  77.31976  36.73873 0.06086957 0.0020533622                77
#>  7: -5.712982 128.00000  48.47419 0.04347826 0.0033028091               128
#>  8: -3.605670  93.84445  44.47628 0.06086957 0.0271692234                93
#>  9: -8.024029  71.78194  11.65893 0.06086957 0.0003274978                71
#> 10: -8.055964 128.00000  62.69488 0.11304348 0.0003172046               128
#>     x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#>  1:                  1            0.011 2022-11-18 12:12:02        1        0
#>  2:                  1            0.013 2022-11-18 12:12:02        2        0
#>  3:                 64            0.011 2022-11-18 12:12:03        3        0
#>  4:                 60            0.013 2022-11-18 12:12:03        4        0
#>  5:                 54            0.012 2022-11-18 12:12:03        5        0
#>  6:                 36            0.013 2022-11-18 12:12:03        6        0
#>  7:                 48            0.012 2022-11-18 12:12:03        7        0
#>  8:                 44            0.012 2022-11-18 12:12:03        8        0
#>  9:                 11            0.012 2022-11-18 12:12:03        9        0
#> 10:                 62            0.012 2022-11-18 12:12:04       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"))