Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Strategy

Subclass that implements CMA-ES calling adagio::pureCMAES() from package adagio.

Source

Hansen N (2016). “The CMA Evolution Strategy: A Tutorial.” 1604.00772.

Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

TunerCmaes$new()
mlr_tuners$get("cmaes")
tnr("cmaes")

Parameters

sigma

numeric(1)

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.

See also

Package mlr3hyperband for hyperband tuning.

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

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerCmaes

Methods

Public methods

• TunerCmaes$new()

• TunerCmaes$clone()

Inherited methods

• mlr3tuning::Tuner$format()

• mlr3tuning::Tuner$print()

• mlr3tuning::TunerFromOptimizer$optimize()

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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

library(data.table)

# retrieve task
task = tsk("pima")

# 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))
)

# hyperparameter tuning on the pima indians diabetes data set
instance = tune(
  method = "cmaes",
  task = task,
  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        2  46.62247          <list[4]> <list[3]>  0.2226562

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp  minsplit minbucket classif.ce  x_domain_cp x_domain_minsplit
#>  1: -9.210340 128.00000  30.73779  0.2929688 0.0001000000               128
#>  2: -7.047217 111.00733  38.21590  0.2343750 0.0008698260               111
#>  3: -8.779414 128.00000  57.35737  0.2929688 0.0001538682               128
#>  4: -3.511509 108.14531  64.00000  0.2929688 0.0298518308               108
#>  5: -9.210340   2.00000  46.62247  0.2226562 0.0001000000                 2
#>  6: -9.210340   2.00000  48.16649  0.2226562 0.0001000000                 2
#>  7: -9.210340  28.75251  41.18789  0.2343750 0.0001000000                28
#>  8: -9.210340  38.86257  12.30461  0.2578125 0.0001000000                38
#>  9: -9.210340   2.00000  55.84191  0.2265625 0.0001000000                 2
#> 10: -9.210340  56.06075  43.25456  0.2343750 0.0001000000                56
#>     x_domain_minbucket runtime_learners           timestamp batch_nr
#>  1:                 30            0.011 2021-09-16 04:23:14        1
#>  2:                 38            0.011 2021-09-16 04:23:14        2
#>  3:                 57            0.012 2021-09-16 04:23:14        3
#>  4:                 64            0.012 2021-09-16 04:23:14        4
#>  5:                 46            0.014 2021-09-16 04:23:15        5
#>  6:                 48            0.013 2021-09-16 04:23:15        6
#>  7:                 41            0.010 2021-09-16 04:23:15        7
#>  8:                 12            0.011 2021-09-16 04:23:15        8
#>  9:                 55            0.010 2021-09-16 04:23:15        9
#> 10:                 43            0.012 2021-09-16 04:23:15       10
#>          resample_result
#>  1: <ResampleResult[20]>
#>  2: <ResampleResult[20]>
#>  3: <ResampleResult[20]>
#>  4: <ResampleResult[20]>
#>  5: <ResampleResult[20]>
#>  6: <ResampleResult[20]>
#>  7: <ResampleResult[20]>
#>  8: <ResampleResult[20]>
#>  9: <ResampleResult[20]>
#> 10: <ResampleResult[20]>

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