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.

## Installation

Install the last release from CRAN:

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$new( fun = fun, domain = domain, codomain = codomain, properties = "deterministic" ) # Define termination criterion terminator = trm("evals", n_evals = 10) # create optimization instance instance = OptimInstanceSingleCrit$new(
objective = objective,
terminator = terminator
)

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$archive) ## x1 x2 y timestamp batch_nr x_domain_x1 x_domain_x2 ## 1: -4.689827 -1.278761 -37.716445 2022-11-18 11:17:17 1 -4.689827 -1.278761 ## 2: -5.930364 -4.400474 -54.851999 2022-11-18 11:17:17 2 -5.930364 -4.400474 ## 3: 7.170817 -1.519948 -18.927907 2022-11-18 11:17:17 3 7.170817 -1.519948 ## 4: 2.045200 -1.519948 7.807403 2022-11-18 11:17:17 4 2.045200 -1.519948 ## 5: 2.045200 -2.064742 9.123250 2022-11-18 11:17:17 5 2.045200 -2.064742 ## 6: 2.045200 -2.064742 9.123250 2022-11-18 11:17:17 6 2.045200 -2.064742 ## 7: 2.045201 -2.064742 9.123250 2022-11-18 11:17:17 7 2.045201 -2.064742 ## 8: 2.045199 -2.064742 9.123250 2022-11-18 11:17:17 8 2.045199 -2.064742 ## 9: 2.045200 -2.064741 9.123248 2022-11-18 11:17:17 9 2.045200 -2.064741 ## 10: 2.045200 -2.064743 9.123252 2022-11-18 11:17:17 10 2.045200 -2.064743 ### Quick optimization with bb_optimize # define the objective function fun = function(xs) { c(y1 = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10) } # optimize function with random search result = bb_optimize(fun, method = "random_search", lower = c(-10, -5), upper = c(10, 5), max_evals = 100) # optimized parameters result$par
##           x1       x2
## 1: -7.982537 4.273021
# optimal outcome
result\$value
##        y1
## -142.5479