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Subclass for tuning w.r.t. fixed design points.

We simply search over a set of points fully specified by the user. The points in the design are evaluated in order as given.

Dictionary

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

tnr("design_points")

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::OptimizerDesignPoints which can be applied on any black box optimization problem. See also the documentation of bbotk.

Parameters

batch_size

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

design

data.table::data.table
Design points to try in search, one per row.

Resources

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").

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerDesignPoints

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

TunerDesignPoints$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),
  minsplit = to_tune(2, 128),
  minbucket = to_tune(1, 64)
)

# create design
design = mlr3misc::rowwise_table(
  ~cp,   ~minsplit,  ~minbucket,
  0.1,   2,          64,
  0.01,  64,         32,
  0.001, 128,        1
)

# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
  method = tnr("design_points", design = design),
  task = tsk("penguins"),
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce")
)

# best performing hyperparameter configuration
instance$result
#>      cp minsplit minbucket learner_param_vals  x_domain classif.ce
#> 1: 0.01       64        32          <list[4]> <list[3]> 0.08695652

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>       cp minsplit minbucket classif.ce x_domain_cp x_domain_minsplit
#> 1: 0.100        2        64 0.11304348       0.100                 2
#> 2: 0.010       64        32 0.08695652       0.010                64
#> 3: 0.001      128         1 0.08695652       0.001               128
#>    x_domain_minbucket runtime_learners           timestamp batch_nr warnings
#> 1:                 64            0.013 2022-11-18 12:12:05        1        0
#> 2:                 32            0.014 2022-11-18 12:12:05        2        0
#> 3:                  1            0.012 2022-11-18 12:12:05        3        0
#>    errors      resample_result
#> 1:      0 <ResampleResult[21]>
#> 2:      0 <ResampleResult[21]>
#> 3:      0 <ResampleResult[21]>

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