TuningInstanceAsyncSingleCrit {mlr3tuning}

Single Criterion Tuning with Rush

Description

The TuningInstanceAsyncSingleCrit specifies a tuning problem for a TunerAsync. The function ti_async() creates a TuningInstanceAsyncSingleCrit and the function tune() creates an instance internally.

Details

The instance contains an ObjectiveTuningAsync object that encodes the black box objective function a Tuner has to optimize. The instance allows the basic operations of querying the objective at design points (⁠$eval_async()⁠). This operation is usually done by the Tuner. Hyperparameter configurations are asynchronously sent to workers and evaluated by calling mlr3::resample(). The evaluated hyperparameter configurations are stored in the ArchiveAsyncTuning (⁠$archive⁠). Before a batch is evaluated, the bbotk::Terminator is queried for the remaining budget. If the available budget is exhausted, an exception is raised, and no further evaluations can be performed from this point on. The tuner is also supposed to store its final result, consisting of a selected hyperparameter configuration and associated estimated performance values, by calling the method instance$.assign_result.

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

Analysis

For analyzing the tuning results, it is recommended to pass the ArchiveAsyncTuning to as.data.table(). The returned data table contains the mlr3::ResampleResult for each hyperparameter evaluation.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

• Getting started with hyperparameter optimization.

• Tune a simple classification tree on the Sonar data set.

• Learn about tuning spaces.

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

• Learn more advanced methods with the practical tuning series.

• Simultaneously optimize hyperparameters and use early stopping with XGBoost.

• Make us of proven search space.

• Learn about hotstarting models.

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

Extension Packages

mlr3tuning is extended by the following packages.

• mlr3tuningspaces is a collection of search spaces from scientific articles for commonly used learners.

• mlr3hyperband adds the Hyperband and Successive Halving algorithm.

• mlr3mbo adds Bayesian optimization methods.

Super classes

bbotk::OptimInstance -> bbotk::OptimInstanceAsync -> bbotk::OptimInstanceAsyncSingleCrit -> TuningInstanceAsyncSingleCrit

Active bindings

result_learner_param_vals

(list())
Param values for the optimal learner call.

internal_search_space

(paradox::ParamSet)
The search space containing those parameters that are internally optimized by the mlr3::Learner.

Methods

Public methods

• TuningInstanceAsyncSingleCrit$new()

• TuningInstanceAsyncSingleCrit$assign_result()

• TuningInstanceAsyncSingleCrit$clone()

Inherited methods

• bbotk::OptimInstance$format()
• bbotk::OptimInstanceAsync$clear()
• bbotk::OptimInstanceAsync$print()

Method new()

Creates a new instance of this R6 class.

Usage

TuningInstanceAsyncSingleCrit$new(
  task,
  learner,
  resampling,
  measure = NULL,
  terminator,
  search_space = NULL,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  rush = NULL
)

Arguments

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.

measure

(mlr3::Measure)
Measure to optimize. If NULL, default measure is used.

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.

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.

Method assign_result()

The TunerAsync object writes the best found point and estimated performance value here. For internal use.

Usage

TuningInstanceAsyncSingleCrit$assign_result(xdt, y, learner_param_vals = NULL)

Arguments

xdt

(data.table::data.table())
Hyperparameter values as data.table::data.table(). Each row is one configuration. Contains values in the search space. Can contain additional columns for extra information.

y

(numeric(1))
Optimal outcome.

learner_param_vals

(List of named ⁠list()s⁠)
Fixed parameter values of the learner that are neither part of the

Method clone()

The objects of this class are cloneable with this method.

Usage

TuningInstanceAsyncSingleCrit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.