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$assign_result
.
bbotk::OptimInstance
-> bbotk::OptimInstanceMultiCrit
-> TuningInstanceMultiCrit
result_learner_param_vals
(list()
)
List of param values for the optimal learner call.
new()
Creates a new instance of this R6 class.
This defines the resampled performance of a learner on a task, a feasibility region for the parameters the tuner is supposed to optimize, and a termination criterion.
TuningInstanceMultiCrit$new( task, learner, resampling, measures, terminator, search_space = NULL, store_models = FALSE, check_values = FALSE, store_benchmark_result = TRUE )
task
(mlr3::Task) Task to operate on.
learner
resampling
(mlr3::Resampling) Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits.
measures
(list of mlr3::Measure)
Measures to optimize.
If NULL
, mlr3's default measure is used.
terminator
(Terminator).
search_space
store_models
(logical(1)
)
Store models in benchmark result?
check_values
(logical(1)
)
Should parameters before the evaluation and the results be checked for
validity?
store_benchmark_result
(logical(1)
)
Store benchmark result in archive?
assign_result()
The Tuner object writes the best found points and estimated performance values here. For internal use.
TuningInstanceMultiCrit$assign_result(xdt, ydt, learner_param_vals = NULL)
xdt
(data.table::data.table()
)
x values as data.table
. Each row is one point. Contains the value in
the search space of the TuningInstanceMultiCrit object. Can contain
additional columns for extra information.
ydt
(data.table::data.table()
)
Optimal outcomes, e.g. the Pareto front.
learner_param_vals
(list()
)
Fixed parameter values of the learner that are neither part of the
clone()
The objects of this class are cloneable with this method.
TuningInstanceMultiCrit$clone(deep = FALSE)
deep
Whether to make a deep clone.