auto_tuner: Function for Automatic Tuning
In mlr-org/mlr3tuning: Hyperparameter Optimization for 'mlr3'

auto_tuner

R Documentation

Function for Automatic Tuning

Description

The AutoTuner wraps a mlr3::Learner and augments it with an automatic tuning process for a given set of hyperparameters. The auto_tuner() function creates an AutoTuner object.

Usage

auto_tuner(
  tuner,
  learner,
  resampling,
  measure = NULL,
  term_evals = NULL,
  term_time = NULL,
  terminator = NULL,
  search_space = NULL,
  store_tuning_instance = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  rush = NULL,
  id = NULL
)

Arguments

`tuner`	(Tuner) Optimization algorithm.
`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.
`measure`	(mlr3::Measure) Measure to optimize. 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$param_set).
`store_tuning_instance`	(`logical(1)`) If `TRUE` (default), stores the internally created TuningInstanceBatchSingleCrit with all intermediate results in slot `⁠$tuning_instance⁠`.
`store_benchmark_result`	(`logical(1)`) If `TRUE` (default), store resample result of evaluated hyperparameter configurations in archive as mlr3::BenchmarkResult.
`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.
`id`	(`character(1)`) Identifier for the new instance.

Details

The AutoTuner is a mlr3::Learner which wraps another mlr3::Learner and performs the following steps during ⁠$train()⁠:

The hyperparameters of the wrapped (inner) learner are trained on the training data via resampling. The tuning can be specified by providing a Tuner, a bbotk::Terminator, a search space as paradox::ParamSet, a mlr3::Resampling and a mlr3::Measure.
The best found hyperparameter configuration is set as hyperparameters for the wrapped (inner) learner stored in at$learner. Access the tuned hyperparameters via at$tuning_result.
A final model is fit on the complete training data using the now parametrized wrapped learner. The respective model is available via field at$learner$model.

During ⁠$predict()⁠ the AutoTuner just calls the predict method of the wrapped (inner) learner. A set timeout is disabled while fitting the final model.

Value

AutoTuner.

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.

Nested Resampling

Nested resampling is performed by passing an AutoTuner to mlr3::resample() or mlr3::benchmark(). To access the inner resampling results, set store_tuning_instance = TRUE and execute mlr3::resample() or mlr3::benchmark() with store_models = TRUE (see examples). The mlr3::Resampling passed to the AutoTuner is meant to be the inner resampling, operating on the training set of an arbitrary outer resampling. For this reason, the inner resampling should be not instantiated. If an instantiated resampling is passed, the AutoTuner fails when a row id of the inner resampling is not present in the training set of the outer resampling.

Examples

at = auto_tuner(
  tuner = tnr("random_search"),
  learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

at$train(tsk("pima"))

mlr-org/mlr3tuning documentation built on April 14, 2025, 1 a.m.