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: -4.044809         1        2          <list[4]> <list[3]> 0.06086957

# 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: -2.302585  1.000000   2.00000 0.07826087 0.100000000                  1
#>  2: -4.873326  1.000000   2.00000 0.06956522 0.007647886                  1
#>  3: -2.302585 33.716295 128.00000 0.07826087 0.100000000                 33
#>  4: -2.302585 64.000000 128.00000 0.16521739 0.100000000                 64
#>  5: -2.302585  7.299205  69.73445 0.07826087 0.100000000                  7
#>  6: -2.302585 26.503901  33.07759 0.07826087 0.100000000                 26
#>  7: -2.302585  1.000000  80.52637 0.07826087 0.100000000                  1
#>  8: -5.708454 18.462738   2.00000 0.07826087 0.003317798                 18
#>  9: -4.044809  1.000000   2.00000 0.06086957 0.017513057                  1
#> 10: -4.204426  1.000000   2.00000 0.06086957 0.014929359                  1
#>     x_domain_minsplit runtime_learners           timestamp batch_nr warnings
#>                 <int>            <num>              <POSc>    <int>    <int>
#>  1:                 2            0.010 2024-06-30 09:41:22        1        0
#>  2:                 2            0.010 2024-06-30 09:41:22        2        0
#>  3:               128            0.010 2024-06-30 09:41:22        3        0
#>  4:               128            0.010 2024-06-30 09:41:22        4        0
#>  5:                69            0.010 2024-06-30 09:41:22        5        0
#>  6:                33            0.010 2024-06-30 09:41:22        6        0
#>  7:                80            0.009 2024-06-30 09:41:22        7        0
#>  8:                 2            0.013 2024-06-30 09:41:22        8        0
#>  9:                 2            0.009 2024-06-30 09:41:22        9        0
#> 10:                 2            0.009 2024-06-30 09:41:22       10        0
#>     errors  resample_result
#>      <int>           <list>
#>  1:      0 <ResampleResult>
#>  2:      0 <ResampleResult>
#>  3:      0 <ResampleResult>
#>  4:      0 <ResampleResult>
#>  5:      0 <ResampleResult>
#>  6:      0 <ResampleResult>
#>  7:      0 <ResampleResult>
#>  8:      0 <ResampleResult>
#>  9:      0 <ResampleResult>
#> 10:      0 <ResampleResult>

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