TunerBatch using Model Based Optimization — mlr_tuners

TunerMbo class that implements Model Based Optimization (MBO). This is a minimal interface internally passing on to OptimizerMbo. For additional information and documentation see OptimizerMbo.

Defaults

All components have sensible defaults. For more information on the defaults for loop_function, surrogate, acq_function, acq_optimizer, and result_assigner, see mbo_defaults.

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerMbo

Active bindings

loop_function: (loop_function | NULL)
Loop function determining the MBO flavor.
surrogate: (Surrogate | NULL)
The surrogate.
acq_function: (AcqFunction | NULL)
The acquisition function.
acq_optimizer: (AcqOptimizer | NULL)
The acquisition function optimizer.
args: (named list())
Further arguments passed to the loop_function. For example, random_interleave_iter.
result_assigner: (ResultAssigner | NULL)
The result assigner.
param_classes: (character())
Supported parameter classes that the optimizer can optimize. Determined based on the surrogate and the acq_optimizer. This corresponds to the values given by a paradox::ParamSet's $class field.
properties: (character())
Set of properties of the optimizer. Must be a subset of bbotk_reflections$optimizer_properties. MBO in principle is very flexible and by default we assume that the optimizer has all properties. When fully initialized, properties are determined based on the loop, e.g., the loop_function, and surrogate.
packages: (character())
Set of required packages. A warning is signaled prior to optimization if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace(). Required packages are determined based on the acq_function, surrogate and the acq_optimizer.

Methods

Inherited methods

`TunerMbo$new()`

Creates a new instance of this R6 class.

Note that all the parameters below are simply passed to the OptimizerMbo and the respective fields are simply (settable) active bindings to the fields of the OptimizerMbo.

Usage

TunerMbo$new(
  loop_function = NULL,
  surrogate = NULL,
  acq_function = NULL,
  acq_optimizer = NULL,
  args = NULL,
  result_assigner = NULL
)

Arguments

loop_function: (loop_function | NULL)
Loop function determining the MBO flavor.
surrogate: (Surrogate | NULL)
The surrogate.
acq_function: (AcqFunction | NULL)
The acquisition function.
acq_optimizer: (AcqOptimizer | NULL)
The acquisition function optimizer.
args: (named list())
Further arguments passed to the loop_function. For example, random_interleave_iter.
result_assigner: (ResultAssigner | NULL)
The result assigner.

`TunerMbo$print()`

Print method.

Usage

TunerMbo$print()

Returns

(character()).

`TunerMbo$reset()`

Reset the tuner. Sets the following fields to NULL: loop_function, surrogate, acq_function, acq_optimizer, args, result_assigner

Usage

TunerMbo$reset()

`TunerMbo$clone()`

The objects of this class are cloneable with this method.

Usage

TunerMbo$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# \donttest{
if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {

  library(mlr3)
  library(mlr3tuning)

  # single-objective
  task = tsk("wine")
  learner = lrn("classif.rpart", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE))
  resampling = rsmp("cv", folds = 3)
  measure = msr("classif.acc")

  instance = TuningInstanceBatchSingleCrit$new(
    task = task,
    learner = learner,
    resampling = resampling,
    measure = measure,
    terminator = trm("evals", n_evals = 5))

  tnr("mbo")$optimize(instance)

  # multi-objective
  task = tsk("wine")
  learner = lrn("classif.rpart", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE))
  resampling = rsmp("cv", folds = 3)
  measures = msrs(c("classif.acc", "selected_features"))

  instance = TuningInstanceBatchMultiCrit$new(
    task = task,
    learner = learner,
    resampling = resampling,
    measures = measures,
    terminator = trm("evals", n_evals = 5),
    store_models = TRUE) # required due to selected features

  tnr("mbo")$optimize(instance)
}
#>           cp learner_param_vals  x_domain classif.acc selected_features
#>        <num>             <list>    <list>       <num>             <num>
#> 1: -6.372912          <list[2]> <list[1]>   0.8817326          2.666667
#> 2: -1.767742          <list[2]> <list[1]>   0.8539548          2.000000
#> 3: -8.675497          <list[2]> <list[1]>   0.8817326          2.666667
#> 4: -7.479440          <list[2]> <list[1]>   0.8817326          2.666667
# }