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Subclass for random search tuning.

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

Details

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

Dictionary

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

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.

Optimizer

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

Parameters

batch_size

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

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 -> TunerRandomSearch

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

TunerRandomSearch$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(
  method = "random_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: -6.63127          <list[2]> <list[1]> 0.02608696

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp classif.ce  x_domain_cp runtime_learners           timestamp
#>  1: -6.631270 0.02608696 0.0013184875            0.012 2022-11-18 12:12:18
#>  2: -7.226346 0.02608696 0.0007271733            0.013 2022-11-18 12:12:18
#>  3: -5.783154 0.02608696 0.0030789879            0.012 2022-11-18 12:12:18
#>  4: -4.486889 0.03478261 0.0112556078            0.012 2022-11-18 12:12:18
#>  5: -5.827162 0.02608696 0.0029464259            0.013 2022-11-18 12:12:18
#>  6: -4.259158 0.03478261 0.0141341961            0.012 2022-11-18 12:12:18
#>  7: -4.373086 0.03478261 0.0126122550            0.013 2022-11-18 12:12:19
#>  8: -3.757827 0.03478261 0.0233343903            0.013 2022-11-18 12:12:19
#>  9: -2.583898 0.03478261 0.0754791796            0.011 2022-11-18 12:12:19
#> 10: -6.081652 0.02608696 0.0022843996            0.011 2022-11-18 12:12:19
#>     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"))