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():

TunerGridSearch$new()
mlr_tuners$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.

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").

See also

Super classes

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

Methods

Public methods

Inherited methods

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.

Examples

# retrieve task task = tsk("pima") # load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)) # hyperparameter tuning on the pima indians diabetes data set instance = tune( method = "grid_search", task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), term_evals = 10 ) # best performing hyperparameter configuration instance$result
#> cp learner_param_vals x_domain classif.ce #> 1: -2.302585 <list[2]> <list[1]> 0.2578125
# all evaluated hyperparameter configuration as.data.table(instance$archive)
#> cp classif.ce x_domain_cp runtime_learners timestamp #> 1: -5.372699 0.3164062 0.0046415888 0.013 2021-09-16 04:23:19 #> 2: -7.675284 0.3203125 0.0004641589 0.013 2021-09-16 04:23:19 #> 3: -2.302585 0.2578125 0.1000000000 0.010 2021-09-16 04:23:19 #> 4: -3.837642 0.2695312 0.0215443469 0.011 2021-09-16 04:23:19 #> 5: -9.210340 0.3203125 0.0001000000 0.011 2021-09-16 04:23:19 #> 6: -6.140227 0.3164062 0.0021544347 0.011 2021-09-16 04:23:19 #> 7: -6.907755 0.3203125 0.0010000000 0.013 2021-09-16 04:23:20 #> 8: -8.442812 0.3203125 0.0002154435 0.014 2021-09-16 04:23:20 #> 9: -3.070113 0.2578125 0.0464158883 0.011 2021-09-16 04:23:20 #> 10: -4.605170 0.2968750 0.0100000000 0.012 2021-09-16 04:23:20 #> batch_nr resample_result #> 1: 1 <ResampleResult[20]> #> 2: 2 <ResampleResult[20]> #> 3: 3 <ResampleResult[20]> #> 4: 4 <ResampleResult[20]> #> 5: 5 <ResampleResult[20]> #> 6: 6 <ResampleResult[20]> #> 7: 7 <ResampleResult[20]> #> 8: 8 <ResampleResult[20]> #> 9: 9 <ResampleResult[20]> #> 10: 10 <ResampleResult[20]>
# fit final model on complete data set learner$param_set$values = instance$result_learner_param_vals learner$train(task)