Hyperparameter Tuning with Design Points

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::OptimizerBatchDesignPoints 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

There are several sections about hyperparameter optimization in the mlr3book.

• Getting started with hyperparameter optimization.

• An overview of all tuners can be found on our website.

• Tune a support vector machine on the Sonar data set.

• Learn about tuning spaces.

• Estimate the model performance with nested resampling.

• Learn about multi-objective optimization.

• Simultaneously optimize hyperparameters and use early stopping with XGBoost.

• Automate the tuning.

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

• Learn more advanced methods with the Practical Tuning Series.

• Learn about hotstarting models.

• Run the default hyperparameter configuration of learners as a baseline.

• Use the Hyperband optimizer with different budget parameters.

The cheatsheet summarizes the most important functions of mlr3tuning.

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::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerBatchDesignPoints

Methods

TunerBatchDesignPoints$new()

TunerBatchDesignPoints$clone(deep = FALSE)

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(
  tuner = tnr("design_points", design = design),
  task = tsk("penguins"),
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce")
)

# best performing hyperparameter configuration
instance$result
#>       cp minbucket minsplit learner_param_vals  x_domain classif.ce
#>    <num>     <num>    <num>             <list>    <list>      <num>
#> 1:  0.01        32       64          <list[4]> <list[3]> 0.07826087

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>       cp minbucket minsplit classif.ce runtime_learners           timestamp
#>    <num>     <num>    <num>      <num>            <num>              <POSc>
#> 1: 0.100        64        2 0.09565217            0.006 2024-12-18 10:06:55
#> 2: 0.010        32       64 0.07826087            0.007 2024-12-18 10:06:55
#> 3: 0.001         1      128 0.07826087            0.005 2024-12-18 10:06:55
#>    warnings errors  x_domain batch_nr  resample_result
#>       <int>  <int>    <list>    <int>           <list>
#> 1:        0      0 <list[3]>        1 <ResampleResult>
#> 2:        0      0 <list[3]>        2 <ResampleResult>
#> 3:        0      0 <list[3]>        3 <ResampleResult>

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