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 bbotk::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::OptimizerBatchCmaes which can be applied on any black box optimization problem. See also the documentation of bbotk.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

• An overview of all tuners can be found on our website.

• Learn more about tuners.

The gallery features a collection of case studies and demos about optimization.

• Use the Hyperband optimizer with different budget parameters.

See also

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

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerBatchCmaes

Methods

Public methods

• TunerBatchCmaes$new()

• TunerBatchCmaes$clone()

Inherited methods

• mlr3tuning::Tuner$format()
• mlr3tuning::Tuner$help()
• mlr3tuning::Tuner$print()
• mlr3tuning::TunerBatchFromOptimizerBatch$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

TunerBatchCmaes$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerBatchCmaes$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(
  tuner = tnr("cmaes"),
  task = tsk("penguins"),
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10)

# best performing hyperparameter configuration
instance$result
#>           cp minbucket minsplit learner_param_vals  x_domain classif.ce
#>        <num>     <num>    <num>             <list>    <list>      <num>
#> 1: -2.302585  18.54552 102.0216          <list[4]> <list[3]> 0.07826087

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp minbucket  minsplit classif.ce  x_domain_cp x_domain_minbucket
#>         <num>     <num>     <num>      <num>        <num>              <int>
#>  1: -9.071539 55.670870 128.00000 0.10434783 0.0001148896                 55
#>  2: -2.302585 18.545516 102.02161 0.07826087 0.1000000000                 18
#>  3: -6.928636 64.000000 128.00000 0.13913043 0.0009793354                 64
#>  4: -4.101324 35.825608 128.00000 0.07826087 0.0165507468                 35
#>  5: -8.413512  1.000000 128.00000 0.07826087 0.0002218493                  1
#>  6: -4.024649 64.000000  91.40801 0.13913043 0.0178696947                 64
#>  7: -6.359513 35.189353 128.00000 0.07826087 0.0017302090                 35
#>  8: -2.464699 57.818568 128.00000 0.11304348 0.0850344183                 57
#>  9: -7.284711  1.069881 128.00000 0.07826087 0.0006859466                  1
#> 10: -2.302585 50.401641 128.00000 0.07826087 0.1000000000                 50
#>     x_domain_minsplit runtime_learners           timestamp warnings errors
#>                 <int>            <num>              <POSc>    <int>  <int>
#>  1:               128            0.006 2024-09-11 07:59:34        0      0
#>  2:               102            0.005 2024-09-11 07:59:34        0      0
#>  3:               128            0.006 2024-09-11 07:59:34        0      0
#>  4:               128            0.007 2024-09-11 07:59:34        0      0
#>  5:               128            0.005 2024-09-11 07:59:34        0      0
#>  6:                91            0.005 2024-09-11 07:59:35        0      0
#>  7:               128            0.006 2024-09-11 07:59:35        0      0
#>  8:               128            0.005 2024-09-11 07:59:35        0      0
#>  9:               128            0.006 2024-09-11 07:59:35        0      0
#> 10:               128            0.005 2024-09-11 07:59:35        0      0
#>     batch_nr  resample_result
#>        <int>           <list>
#>  1:        1 <ResampleResult>
#>  2:        2 <ResampleResult>
#>  3:        3 <ResampleResult>
#>  4:        4 <ResampleResult>
#>  5:        5 <ResampleResult>
#>  6:        6 <ResampleResult>
#>  7:        7 <ResampleResult>
#>  8:        8 <ResampleResult>
#>  9:        9 <ResampleResult>
#> 10:       10 <ResampleResult>

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