mlr3tuning (version 0.9.0)

mlr_tuners_cmaes: Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Strategy

Description

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

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.

Super classes

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

Methods

Public methods

• TunerCmaes$new()

• TunerCmaes$clone()

• mlr3tuning::Tuner$format()

• 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.

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

Examples

# NOT RUN {
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

# all evaluated hyperparameter configuration
as.data.table(instance$archive)

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