Specifies a general multi-criteria tuning scenario, including objective function and archive for Tuners to act upon. This class stores an ObjectiveTuning object that encodes the black box objective function which a Tuner has to optimize. It allows the basic operations of querying the objective at design points ($eval_batch()), storing the evaluations in the internal Archive and accessing the final result ($result).

Evaluations of hyperparameter configurations are performed in batches by calling mlr3::benchmark() internally. 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$assign_result. ## Super classes bbotk::OptimInstance -> bbotk::OptimInstanceMultiCrit -> TuningInstanceMultiCrit ## Active bindings result_learner_param_vals (list()) List of param values for the optimal learner call. ## Methods ### Public methods Inherited methods ### Method new() Creates a new instance of this R6 class. This defines the resampled performance of a learner on a task, a feasibility region for the parameters the tuner is supposed to optimize, and a termination criterion. #### Usage TuningInstanceMultiCrit$new(
learner,
resampling,
measures,
terminator,
search_space = NULL,
store_models = FALSE,
check_values = FALSE,
store_benchmark_result = TRUE
)

#### Arguments

task

learner
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.

measures

(list of mlr3::Measure)
Measures to optimize. If NULL, mlr3's default measure is used.

terminator

(Terminator).

search_space

Hyperparameter search space. If NULL, the search space is constructed from the TuneToken in the ParamSet of the learner.

store_models

(logical(1))
If FALSE (default), the fitted models are not stored in the mlr3::BenchmarkResult. If store_benchmark_result = FALSE, the models are only stored temporarily and not accessible after the tuning. This combination might be useful for measures that require a model.

check_values

(logical(1))
Should parameters before the evaluation and the results be checked for validity?

store_benchmark_result

(logical(1))
If TRUE (default), stores the mlr3::BenchmarkResult in archive.

### Method assign_result()

The Tuner object writes the best found points and estimated performance values here. For internal use.

#### Arguments

deep

Whether to make a deep clone.

## Examples

library(data.table)

# define search space
search_space = ps(
cp = p_dbl(lower = 0.001, upper = 0.1),
minsplit = p_int(lower = 1, upper = 10)
)

# initialize instance
instance = TuningInstanceMultiCrit$new( task = tsk("iris"), learner = lrn("classif.rpart"), resampling = rsmp("holdout"), measure = msrs(c("classif.ce", "classif.acc")), search_space = search_space, terminator = trm("evals", n_evals = 5) ) # generate design design = data.table(cp = c(0.05, 0.01), minsplit = c(5, 3)) # eval design instance$eval_batch(design)

# show archive
instance\$archive
#> <ArchiveTuning>
#>      cp minsplit classif.ce classif.acc runtime_learners           timestamp
#> 1: 0.05        5        0.1         0.9            0.009 2021-09-16 04:23:07
#> 2: 0.01        3        0.1         0.9            0.011 2021-09-16 04:23:07
#>    batch_nr      resample_result
#> 1:        1 <ResampleResult[20]>
#> 2:        1 <ResampleResult[20]>