Function for Tuning a Learner — tune • mlr3tuning

Function to tune a mlr3::Learner. The function internally creates a TuningInstanceBatchSingleCrit or TuningInstanceBatchMultiCrit which describes the tuning problem. It executes the tuning with the Tuner (tuner) and returns the result with the tuning instance ($result). The ArchiveBatchTuning and ArchiveAsyncTuning ($archive) stores all evaluated hyperparameter configurations and performance scores.

You can find an overview of all tuners on our website.

Usage

tune(
  tuner,
  task,
  learner,
  resampling,
  measures = NULL,
  term_evals = NULL,
  term_time = NULL,
  terminator = NULL,
  search_space = NULL,
  store_benchmark_result = TRUE,
  internal_search_space = NULL,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  rush = NULL
)

Arguments

tuner: (Tuner)
Optimization algorithm.
task: (mlr3::Task)
Task to operate on.
learner: (mlr3::Learner)
Learner to tune.
resampling: (mlr3::Resampling)
Resampling that is used to evaluate the performance of the hyperparameter configurations. Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits. Already instantiated resamplings are kept unchanged. Specialized Tuner change the resampling e.g. to evaluate a hyperparameter configuration on different data splits. This field, however, always returns the resampling passed in construction.
measures: (mlr3::Measure or list of mlr3::Measure)
A single measure creates a TuningInstanceBatchSingleCrit and multiple measures a TuningInstanceBatchMultiCrit. If NULL, default measure is used.
term_evals: (integer(1))
Number of allowed evaluations. Ignored if terminator is passed.
term_time: (integer(1))
Maximum allowed time in seconds. Ignored if terminator is passed.
terminator: (bbotk::Terminator)
Stop criterion of the tuning process.
search_space: (paradox::ParamSet)
Hyperparameter search space. If NULL (default), the search space is constructed from the paradox::TuneToken of the learner's parameter set (learner$param_set).
store_benchmark_result: (logical(1))
If TRUE (default), store resample result of evaluated hyperparameter configurations in archive as mlr3::BenchmarkResult.
internal_search_space: (paradox::ParamSet or NULL)
The internal search space.
store_models: (logical(1))
If TRUE, fitted models are stored in the benchmark result (archive$benchmark_result). If store_benchmark_result = FALSE, models are only stored temporarily and not accessible after the tuning. This combination is needed for measures that require a model.
check_values: (logical(1))
If TRUE, hyperparameter values are checked before evaluation and performance scores after. If FALSE (default), values are unchecked but computational overhead is reduced.
callbacks: (list of mlr3misc::Callback)
List of callbacks.
rush: (Rush)
If a rush instance is supplied, the tuning runs without batches.

Value

TuningInstanceBatchSingleCrit | TuningInstanceBatchMultiCrit

Details

The mlr3::Task, mlr3::Learner, mlr3::Resampling, mlr3::Measure and bbotk::Terminator are used to construct a TuningInstanceBatchSingleCrit. If multiple performance mlr3::Measures are supplied, a TuningInstanceBatchMultiCrit is created. The parameter term_evals and term_time are shortcuts to create a bbotk::Terminator. If both parameters are passed, a bbotk::TerminatorCombo is constructed. For other Terminators, pass one with terminator. If no termination criterion is needed, set term_evals, term_time and terminator to NULL. The search space is created from paradox::TuneToken or is supplied by search_space.

Default Measures

If no measure is passed, the default measure is used. The default measure depends on the task type.

Task	Default Measure	Package
`"classif"`	`"classif.ce"`	mlr3
`"regr"`	`"regr.mse"`	mlr3
`"surv"`	`"surv.cindex"`	mlr3proba
`"dens"`	`"dens.logloss"`	mlr3proba
`"classif_st"`	`"classif.ce"`	mlr3spatial
`"regr_st"`	`"regr.mse"`	mlr3spatial
`"clust"`	`"clust.dunn"`	mlr3cluster

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.

Analysis

For analyzing the tuning results, it is recommended to pass the ArchiveBatchTuning to as.data.table(). The returned data table is joined with the benchmark result which adds the mlr3::ResampleResult for each hyperparameter evaluation.

The archive provides various getters (e.g. $learners()) to ease the access. All getters extract by position (i) or unique hash (uhash). For a complete list of all getters see the methods section.

The benchmark result ($benchmark_result) allows to score the hyperparameter configurations again on a different measure. Alternatively, measures can be supplied to as.data.table().

The mlr3viz package provides visualizations for tuning results.

Examples

# Hyperparameter optimization on the Palmer Penguins data set
task = tsk("pima")

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

# Run tuning
instance = tune(
  tuner = tnr("random_search", batch_size = 2),
  task = tsk("pima"),
  learner = learner,
  resampling = rsmp ("holdout"),
  measures = msr("classif.ce"),
  terminator = trm("evals", n_evals = 4)
)

# Set optimal hyperparameter configuration to learner
learner$param_set$values = instance$result_learner_param_vals

# Train the learner on the full data set
learner$train(task)

# Inspect all evaluated configurations
as.data.table(instance$archive)
#>           cp classif.ce runtime_learners           timestamp warnings errors
#>        <num>      <num>            <num>              <POSc>    <int>  <int>
#> 1: -6.062105  0.2734375            0.006 2024-12-18 10:10:09        0      0
#> 2: -5.192975  0.2695312            0.006 2024-12-18 10:10:09        0      0
#> 3: -2.592958  0.2304688            0.006 2024-12-18 10:10:09        0      0
#> 4: -5.708848  0.2734375            0.006 2024-12-18 10:10:09        0      0
#>     x_domain batch_nr  resample_result
#>       <list>    <int>           <list>
#> 1: <list[1]>        1 <ResampleResult>
#> 2: <list[1]>        1 <ResampleResult>
#> 3: <list[1]>        2 <ResampleResult>
#> 4: <list[1]>        2 <ResampleResult>