mlr3tuning (version 0.5.0)

mlr_tuners_grid_search: TunerGridSearch

Description

Subclass for grid search tuning.

The grid is constructed as a Cartesian product over discretized values per parameter, see paradox::generate_design_grid(). The points of the grid are evaluated in a random order.

Dictionary

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

mlr_tuners$get("grid_search")
tnr("grid_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

resolution

integer(1) Resolution of the grid, see paradox::generate_design_grid().

param_resolutions

named integer() Resolution per parameter, named by parameter ID, see paradox::generate_design_grid().

batch_size

integer(1) Maximum number of points to try in a batch.

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerGridSearch

Methods

Public methods

• TunerGridSearch$new()

• TunerGridSearch$clone()

• mlr3tuning::Tuner$format()

• mlr3tuning::Tuner$print()

• mlr3tuning::TunerFromOptimizer$optimize()

Method new()

Creates a new instance of this R6 class.

Usage

TunerGridSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerGridSearch$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_nloptr, mlr_tuners_random_search

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("grid_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
# }