TunerAsync using Asynchronous Decentralized Bayesian Optimization

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

lambda: numeric(1)
Value used for sampling the lambda for each worker from an exponential distribution.
rate: numeric(1)
Rate of the exponential decay.
period: integer(1)
Period of the exponential decay.
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.

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.

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

Inherited methods

`TunerADBO$new()`

Creates a new instance of this R6 class.

Usage

TunerADBO$new()

`TunerADBO$print()`

Print method.

Usage

TunerADBO$print()

Returns

(character()).

`TunerADBO$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()

`TunerADBO$clone()`

The objects of this class are cloneable with this method.

Usage

TunerADBO$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

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))

    mirai::daemons(2)
    rush::rush_plan(n_workers=2, worker_type = "mirai")

    tnr("adbo", design_size = 4, n_workers = 2)$optimize(instance)
    mirai::daemons(0)
  } else {
    message("Redis server is not available.\nPlease set up Redis prior to running the example.")
  }
}
# }