mlr3tuning (version 0.5.0)

mlr_tuners_random_search: TunerRandomSearch

Description

Subclass for random search tuning.

The random points are sampled by paradox::generate_design_random().

Dictionary

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

mlr_tuners$get("random_search")
tnr("random_search")

Parallelization

In order to support general termination criteria and parallelization, we evaluate points in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria. A batch contains of batch_size times resampling$iters jobs. E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can utilize up to 50 cores.

Parallelization is supported via package future (see mlr3::benchmark()'s section on parallelization for more details).

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Parameters

algorithm

character(1)

x0

numeric()

eval_g_ineq

function()

xtol_rel

numeric(1)

xtol_abs

numeric(1)

ftol_rel

numeric(1)

ftol_abs

numeric(1)

For the meaning of the control parameters, see nloptr::nloptr() and nloptr::nloptr.print.options().

The termination conditions stopval, maxtime and maxeval of nloptr::nloptr() are deactivated and replaced by the Terminator subclasses. The x and function value tolerance termination conditions (xtol_rel = 10^-4, xtol_abs = rep(0.0, length(x0)), ftol_rel = 0.0 and ftol_abs = 0.0) are still available and implemented with their package defaults. To deactivate these conditions, set them to -1.

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerRandomSearch

Methods

Public methods

• TunerRandomSearch$new()

• TunerRandomSearch$clone()

• mlr3tuning::Tuner$format()

• mlr3tuning::Tuner$print()

• mlr3tuning::TunerFromOptimizer$optimize()

Method new()

Creates a new instance of this R6 class.

Usage

TunerRandomSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Package mlr3hyperband for hyperband tuning.

Other Tuner: mlr_tuners_cmaes, mlr_tuners_design_points, mlr_tuners_gensa, mlr_tuners_grid_search, mlr_tuners_nloptr

Examples

# NOT RUN {
library(mlr3)
library(paradox)
search_space = ParamSet$new(list(
  ParamDbl$new("cp", lower = 0.001, upper = 0.1)
))
terminator = trm("evals", n_evals = 3)
instance = TuningInstanceSingleCrit$new(
  task = tsk("iris"),
  learner = lrn("classif.rpart"),
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  search_space = search_space,
  terminator = terminator
)
tt = tnr("random_search")

# modifies the instance by reference
tt$optimize(instance)

# returns best configuration and best performance
instance$result

# allows access of data.table of full path of all evaluations
instance$archive
# }

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