The TuningInstanceSingleCrit specifies a tuning problem for Tuners. The function ti() creates a TuningInstanceSingleCrit and the function tune() creates an instance internally.

The instance contains an ObjectiveTuning 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 ($eval_batch()). This operation is usually done by the Tuner. Evaluations of hyperparameter configurations are performed in batches by calling mlr3::benchmark() internally. The evaluated hyperparameter configurations are stored in the Archive ($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$assign_result. ## Resources ## Analysis For analyzing the tuning results, it is recommended to pass the ArchiveTuning to as.data.table(). The returned data table is joined with the benchmark result which adds the mlr3::ResampleResult for each hyperparameter evaluation. The archive provides various getters (e.g.$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 ($benchmark_result) allows to score the hyperparameter configurations again on a different measure. Alternatively, measures can be supplied to as.data.table(). The mlr3viz package provides visualizations for tuning results. ## Super classes bbotk::OptimInstance -> bbotk::OptimInstanceSingleCrit -> TuningInstanceSingleCrit ## Active bindings result_learner_param_vals (list()) Param values for the optimal learner call. ## Methods ### Public methods Inherited methods ### Method new() Creates a new instance of this R6 class. #### Usage task, learner, resampling, measure = NULL, terminator, search_space = NULL, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, allow_hotstart = FALSE, keep_hotstart_stack = FALSE, evaluate_default = FALSE, callbacks = list() ) #### Arguments task (mlr3::Task) Task to operate on. learner (mlr3::Learner) Learner to tune. resampling (mlr3::Resampling) Resampling that is used to evaluated the performance of the hyperparameter configurations. Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits. Already instantiated resamplings are kept unchanged. Specialized Tuner change the resampling e.g. to evaluate a hyperparameter configuration on different data splits. This field, however, always returns the resampling passed in construction. measure (mlr3::Measure) Measure to optimize. If NULL, default measure is used. terminator (Terminator) Stop criterion of the tuning process. search_space (paradox::ParamSet) Hyperparameter search space. If NULL (default), the search space is constructed from the TuneToken of the learner's parameter set (learner$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))

#### 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.

TuningInstanceSingleCrit$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples # Hyperparameter optimization on the Palmer Penguins data set task = tsk("penguins") # Load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE) ) # Construct tuning instance instance = ti( task = task, learner = learner, resampling = rsmp("cv", folds = 3), measures = msr("classif.ce"), terminator = trm("evals", n_evals = 4) ) # Choose optimization algorithm tuner = tnr("random_search", batch_size = 2) # Run tuning tuner$optimize(instance)
#>           cp learner_param_vals  x_domain classif.ce
#> 1: -2.539968          <list[2]> <list[1]> 0.06979405

# 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  x_domain_cp runtime_learners           timestamp
#> 1: -5.635372 0.07274345 0.0035693497            0.034 2022-11-18 12:11:45
#> 2: -2.539968 0.06979405 0.0788689561            0.057 2022-11-18 12:11:45
#> 3: -8.154337 0.07274345 0.0002874859            0.258 2022-11-18 12:11:46
#> 4: -5.188467 0.07274345 0.0055805526            0.035 2022-11-18 12:11:46
#>    batch_nr warnings errors      resample_result
#> 1:        1        0      0 <ResampleResult[21]>
#> 2:        1        0      0 <ResampleResult[21]>
#> 3:        2        0      0 <ResampleResult[21]>
#> 4:        2        0      0 <ResampleResult[21]>