mlr3mbo (version 0.2.9)

mlr_tuners_adbo: TunerAsync using Asynchronous Decentralized Bayesian Optimization

Description

TunerADBO class that implements Asynchronous Decentralized Bayesian Optimization (ADBO). ADBO is a variant of Asynchronous Model Based Optimization (AMBO) that uses AcqFunctionStochasticCB with exponential lambda decay. This is a minimal interface internally passing on to OptimizerAsyncMbo. For additional information and documentation see OptimizerAsyncMbo.

Currently, only single-objective optimization is supported and TunerADBO is considered an experimental feature and API might be subject to changes.

Parameters

initial_design

data.table::data.table()
Initial design of the optimization. If NULL, a design of size design_size is generated with the specified design_function. Default is NULL.

design_size

integer(1)
Size of the initial design if it is to be generated. Default is 100.

design_function

character(1)
Sampling function to generate the initial design. Can be random paradox::generate_design_random, lhs paradox::generate_design_lhs, or sobol paradox::generate_design_sobol. Default is sobol.

n_workers

integer(1)
Number of parallel workers. If NULL, all rush workers specified via rush::rush_plan() are used. Default is NULL.

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerAsync -> mlr3tuning::TunerAsyncFromOptimizerAsync -> TunerADBO

Active bindings

surrogate

(Surrogate | NULL)
The surrogate.

acq_function

(AcqFunction | NULL)
The acquisition function.

acq_optimizer

(AcqOptimizer | NULL)
The acquisition function optimizer.

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

Public methods

• TunerADBO$new()

• TunerADBO$print()

• TunerADBO$reset()

• TunerADBO$clone()

Inherited methods

• mlr3tuning::Tuner$format()
• mlr3tuning::Tuner$help()
• mlr3tuning::TunerAsyncFromOptimizerAsync$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

TunerADBO$new()

---

Method print()

Print method.

Usage

TunerADBO$print()

Returns

(character()).

---

Method reset()

Reset the tuner. Sets the following fields to NULL: surrogate, acq_function, acq_optimizer, result_assigner Resets parameter values design_size and design_function to their defaults.

Usage

TunerADBO$reset()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerADBO$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

• Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization.” In 2023 IEEE 19th International Conference on e-Science (e-Science), 1--10.

Examples

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

    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 = TuningInstanceAsyncSingleCrit$new(
      task = task,
      learner = learner,
      resampling = resampling,
      measure = measure,
      terminator = trm("evals", n_evals = 10))

    rush::rush_plan(n_workers=2)

    tnr("adbo", design_size = 4, n_workers = 2)$optimize(instance)
  } else {
    message("Redis server is not available.\nPlease set up Redis prior to running the example.")
  }
}
# }