Package website: release | dev
This package provides a common framework for optimization including
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.Various optimization methods are already implemented e.g. grid search, random search and generalized simulated annealing.
Install the last release from CRAN:
install.packages("bbotk")Install the development version from GitHub:
remotes::install_github("mlr-org/bbotk")# define objective function
fun = function(xs) {
c(y = - (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
)
# 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$archive)## x1 x2 y timestamp batch_nr x_domain_x1 x_domain_x2
## 1: -4.689827 -1.278761 -37.716445 2021-10-10 18:03:01 1 -4.689827 -1.278761
## 2: -5.930364 -4.400474 -54.851999 2021-10-10 18:03:01 2 -5.930364 -4.400474
## 3: 7.170817 -1.519948 -18.927907 2021-10-10 18:03:01 3 7.170817 -1.519948
## 4: 2.045200 -1.519948 7.807403 2021-10-10 18:03:01 4 2.045200 -1.519948
## 5: 2.045200 -2.064742 9.123250 2021-10-10 18:03:01 5 2.045200 -2.064742
## 6: 2.045200 -2.064742 9.123250 2021-10-10 18:03:01 6 2.045200 -2.064742
## 7: 2.045201 -2.064742 9.123250 2021-10-10 18:03:01 7 2.045201 -2.064742
## 8: 2.045199 -2.064742 9.123250 2021-10-10 18:03:01 8 2.045199 -2.064742
## 9: 2.045200 -2.064741 9.123248 2021-10-10 18:03:01 9 2.045200 -2.064741
## 10: 2.045200 -2.064743 9.123252 2021-10-10 18:03:01 10 2.045200 -2.064743bb_optimizelibrary(bbotk)
# define objective function
fun = function(xs) {
c(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}
# optimize function with random search
result = bb_optimize(fun, method = "random_search", lower = c(-10, -5), upper = c(10, 5),
max_evals = 100)
# optimized parameters
result$par## x1 x2
## 1: -7.982537 4.273021# optimal outcome
result$value## y1
## -142.5479