Hyperparameter Tuning with Grid Search

Subclass for grid search tuning.

Details

The grid is constructed as a Cartesian product over discretized values per parameter, see paradox::generate_design_grid(). If the learner supports hotstarting, the grid is sorted by the hotstart parameter (see also mlr3::HotstartStack). If not, the points of the grid are evaluated in a random order.

Dictionary

This Tuner can be instantiated with the associated sugar function tnr():

tnr("grid_search")

Control 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").

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.

Optimizer

This Tuner is based on bbotk::OptimizerGridSearch which can be applied on any black box optimization problem. See also the documentation of bbotk.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

• Learn more about tuners.

The gallery features a collection of case studies and demos about optimization.

• Use the Hyperband optimizer with different budget parameters.

See also

Other Tuner: mlr_tuners_cmaes, mlr_tuners_design_points, mlr_tuners_gensa, mlr_tuners_irace, mlr_tuners_nloptr, mlr_tuners_random_search, mlr_tuners

Super class

mlr3tuning::Tuner -> TunerGridSearch

Methods

Public methods

• TunerGridSearch$new()

• TunerGridSearch$clone()

Inherited methods

• mlr3tuning::Tuner$format()
• mlr3tuning::Tuner$help()
• mlr3tuning::Tuner$optimize()
• mlr3tuning::Tuner$print()

---

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

# Hyperparameter Optimization

# load learner and set search space
learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE)
)

# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
  tuner = tnr("grid_search"),
  task = tsk("penguins"),
  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: -3.070113          <list[2]> <list[1]> 0.04347826

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp classif.ce  x_domain_cp runtime_learners           timestamp
#>  1: -7.675284 0.06086957 0.0004641589            0.005 2023-11-28 14:29:37
#>  2: -5.372699 0.06086957 0.0046415888            0.006 2023-11-28 14:29:37
#>  3: -3.070113 0.04347826 0.0464158883            0.007 2023-11-28 14:29:37
#>  4: -4.605170 0.07826087 0.0100000000            0.005 2023-11-28 14:29:37
#>  5: -8.442812 0.06086957 0.0002154435            0.007 2023-11-28 14:29:37
#>  6: -6.907755 0.06086957 0.0010000000            0.007 2023-11-28 14:29:37
#>  7: -2.302585 0.04347826 0.1000000000            0.007 2023-11-28 14:29:37
#>  8: -3.837642 0.04347826 0.0215443469            0.007 2023-11-28 14:29:37
#>  9: -9.210340 0.06086957 0.0001000000            0.007 2023-11-28 14:29:37
#> 10: -6.140227 0.06086957 0.0021544347            0.007 2023-11-28 14:29:37
#>     batch_nr warnings errors      resample_result
#>  1:        1        0      0 <ResampleResult[21]>
#>  2:        2        0      0 <ResampleResult[21]>
#>  3:        3        0      0 <ResampleResult[21]>
#>  4:        4        0      0 <ResampleResult[21]>
#>  5:        5        0      0 <ResampleResult[21]>
#>  6:        6        0      0 <ResampleResult[21]>
#>  7:        7        0      0 <ResampleResult[21]>
#>  8:        8        0      0 <ResampleResult[21]>
#>  9:        9        0      0 <ResampleResult[21]>
#> 10:       10        0      0 <ResampleResult[21]>

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