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bbotk - Black-Box Optimization Toolkit

Package website: release | dev

bbotk is a black-box optimization framework for R. It features highly configurable search spaces via the paradox package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in mlr3mbo) and Hyperband (in mlr3hyperband). bbotk is the base package of mlr3tuning, mlr3fselect and miesmuschel.

The package includes the basic building blocks of optimization:

  • Optimizer: Objects of this class allow you to optimize an object of the class OptimInstance.
  • OptimInstance: Defines the optimization problem, consisting of an Objective, the search_space, and a Terminator. All evaluations on the OptimInstance will be automatically stored in its own Archive.
  • Objective: Objects of this class contain the objective function. The class ensures that the objective function is called in the right way and defines, whether the function should be minimized or maximized.
  • Terminator: Objects of this class control the termination of the optimization independent of the optimizer.

Resources

Installation

Install the last release from CRAN:

install.packages("bbotk")

Install the development version from GitHub:

remotes::install_github("mlr-org/bbotk")

Examples

Optimization

# define the objective function
fun = function(xs) {
  - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(
  y = p_dbl(tags = "maximize")
)

# create Objective object
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# Define termination criterion
terminator = trm("evals", n_evals = 10)

# create optimization instance
instance = OptimInstanceBatchSingleCrit$new(
  objective = objective,
  terminator = terminator
)

# load optimizer
optimizer = opt("gensa")

# trigger optimization
optimizer$optimize(instance)
##        x1        x2  x_domain        y
## 1: 2.0452 -2.064743 <list[2]> 9.123252
# best performing configuration
instance$result
##        x1        x2  x_domain        y
## 1: 2.0452 -2.064743 <list[2]> 9.123252
# all evaluated configuration
as.data.table(instance$archive)
##            x1        x2          y           timestamp batch_nr x_domain_x1 x_domain_x2
##  1: -4.689827 -1.278761 -37.716445 2024-06-21 09:34:39        1   -4.689827   -1.278761
##  2: -5.930364 -4.400474 -54.851999 2024-06-21 09:34:39        2   -5.930364   -4.400474
##  3:  7.170817 -1.519948 -18.927907 2024-06-21 09:34:39        3    7.170817   -1.519948
##  4:  2.045200 -1.519948   7.807403 2024-06-21 09:34:39        4    2.045200   -1.519948
##  5:  2.045200 -2.064742   9.123250 2024-06-21 09:34:39        5    2.045200   -2.064742
##  6:  2.045200 -2.064742   9.123250 2024-06-21 09:34:39        6    2.045200   -2.064742
##  7:  2.045201 -2.064742   9.123250 2024-06-21 09:34:39        7    2.045201   -2.064742
##  8:  2.045199 -2.064742   9.123250 2024-06-21 09:34:39        8    2.045199   -2.064742
##  9:  2.045200 -2.064741   9.123248 2024-06-21 09:34:39        9    2.045200   -2.064741
## 10:  2.045200 -2.064743   9.123252 2024-06-21 09:34:39       10    2.045200   -2.064743

Quick optimization with bb_optimize

library(bbotk)

# define the objective function
fun = function(xs) {
  c(y1 = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}

# optimize function with random search
result = bb_optimize(fun, method = "random_search", lower = c(-10, -5), upper = c(10, 5),
  max_evals = 100)

# optimized parameters
result$par
##           x1       x2
## 1: -7.982537 4.273021
# optimal outcome
result$value
##        y1 
## -142.5479

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Version

Install

install.packages('bbotk')

Monthly Downloads

4,348

Version

1.0.1

License

LGPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Marc Becker

Last Published

July 24th, 2024

Functions in bbotk (1.0.1)

Archive

Data Storage
Objective

Objective Function with Domain and Codomain
ContextAsync

Asynchronous Optimization Context
ArchiveAsync

Rush Data Storage
ContextBatch

Batch Optimization Context
ArchiveBatch

Data Table Storage
CallbackAsync

Create Asynchronous Optimization Callback
Codomain

Codomain of Function
CallbackBatch

Create Batch Optimization Callback
ObjectiveRFun

Objective interface with custom R function
OptimInstanceAsyncMultiCrit

Multi Criteria Optimization Instance for Asynchronous Optimization
OptimInstance

Optimization Instance
OptimInstanceBatchSingleCrit

Single Criterion Optimization Instance for Batch Optimization
ObjectiveRFunDt

Objective interface for basic R functions.
ObjectiveRFunMany

Objective Interface with Custom R Function
OptimInstanceAsync

Optimization Instance for Asynchronous Optimization
OptimInstanceBatchMultiCrit

Multi Criteria Optimization Instance for Batch Optimization
OptimInstanceMultiCrit

Multi Criteria Optimization Instance for Batch Optimization
OptimInstanceAsyncSingleCrit

Single Criterion Optimization Instance for Asynchronous Optimization
OptimInstanceSingleCrit

Single Criterion Optimization Instance for Batch Optimization
as_terminator

Convert to a Terminator
OptimInstanceBatch

Optimization Instance for Batch Optimization
OptimizerAsync

Asynchronous Optimizer
bbotk-package

bbotk: Black-Box Optimization Toolkit
bb_optimize

Black-Box Optimization
Optimizer

Optimizer
Progressor

Progressor
Terminator

Abstract Terminator Class
assign_result_default

Default Assign Result Function
is_dominated

Calculate which points are dominated
OptimizerBatch

Batch Optimizer
branin

Branin Function
callback_async

Create Asynchronous Optimization Callback
evaluate_queue_default

Default Evaluation of the Queue
callback_batch

Create Batch Optimization Callback
bbotk.backup

Backup Archive Callback
bbotk_reflections

Reflections for bbotk
mlr_optimizers

Dictionary of Optimizer
bbotk_assertions

Assertion for bbotk objects
mlr_optimizers_async_design_points

Asynchronous Optimization via Design Points
bbotk_worker_loop

Worker loop for Rush
mlr_optimizers_async_grid_search

Asynchronous Optimization via Grid Search
mlr_optimizers_design_points

Optimization via Design Points
mlr_optimizers_focus_search

Optimization via Focus Search
mlr_optimizers_gensa

Optimization via Generalized Simulated Annealing
mlr_optimizers_grid_search

Optimization via Grid Search
mlr_optimizers_irace

Optimization via Iterated Racing
mlr_optimizers_cmaes

Optimization via Covariance Matrix Adaptation Evolution Strategy
mlr_optimizers_async_random_search

Asynchronous Optimization via Random Search
mlr_optimizers_nloptr

Optimization via Non-linear Optimization
mlr_optimizers_random_search

Optimization via Random Search
mlr_terminators_perf_reached

Performance Level Terminator
mlr_terminators_none

None Terminator
mlr_terminators_evals

Terminator that stops after a number of evaluations
mlr_terminators_clock_time

Clock Time Terminator
mlr_terminators_run_time

Run Time Terminator
nds_selection

Best points w.r.t. non dominated sorting with hypervolume contribution.
mlr_terminators

Dictionary of Terminators
oi

Syntactic Sugar for Optimization Instance Construction
optimize_async_default

Default Asynchronous Optimization
reexports

Objects exported from other packages
optimize_batch_default

Default Batch Optimization Function
trafo_xs

Calculate the transformed x-values
search_start

Get start values for optimizers
shrink_ps

Shrink a ParamSet towards a point.
terminated_error

Termination Error
mlr_terminators_combo

Combine Terminators
mlr_terminators_stagnation

Terminator that stops when optimization does not improve
transform_xdt_to_xss

Calculates the transformed x-values
trm

Syntactic Sugar Terminator Construction
mlr_terminators_stagnation_batch

Terminator that stops when optimization does not improve
oi_async

Syntactic Sugar for Asynchronous Optimization Instance Construction
opt

Syntactic Sugar Optimizer Construction