Jakob Richter
Kind of working skeletton!
set.seed(1)
library(R6)
library(checkmate)
devtools::load_all()## Loading BayOptR
## Loading required package: paradox
## Loading required package: data.table
Basic Problem Definition:
fun = function(x) sqrt(x) * sin(x) # Alpine 02
search_space = ParamSet$new(params = list(ParamDbl$new("x", 0, 10)))
target_fun = TargetFun$new(fun, search_space)
design = generate_design_lhs(search_space, 5)
design = data.table(x = design$transpose())
design$y = target_fun$eval(design)MBO Problem:
op = OptProblem$new(
target_fun = target_fun,
design = design,
opt_iterator = OptIteratorBO$new(),
opt_terminator = OptTerminatorSteps$new(5),
surrogate_model = SurrogateModelGPfit$new(design = design),
proposal_generator = ProposalGeneratorSingle$new(
acq_optimizer = AcqOptimizerGenSA$new(control = list(maxit = 100)),
acq_function = AcqFunctionCB$new(lambda = 2)
)
)
res = bayopt(op)
res$surrogate_model$design## x y
## <list> <num>
## 1: <list> 2.5375694
## 2: <list> -1.8290018
## 3: <list> 0.6090158
## 4: <list> 2.6113385
## 5: <list> 0.2521211
## 6: <list> -1.8758919
## 7: <list> -2.1207273
## 8: <list> -2.1706077
## 9: <list> -2.1798936
## 10: <list> -2.1818586
res$surrogate_model$predict(res$surrogate_model$design)## mean se
## <num> <num>
## 1: 2.5375694 7.831234e-14
## 2: -1.8290018 0.000000e+00
## 3: 0.6090158 5.786626e-15
## 4: 2.6113385 0.000000e+00
## 5: 0.2521211 0.000000e+00
## 6: -1.8758919 0.000000e+00
## 7: -2.1207273 0.000000e+00
## 8: -2.1706077 0.000000e+00
## 9: -2.1798936 0.000000e+00
## 10: -2.1818586 0.000000e+00
plot(res$surrogate_model$storage$model)
curve(fun(x*10), add = TRUE, col = "green") # GP_fit does some scalingop = OptProblem$new(
target_fun = target_fun,
design = design,
opt_iterator = OptIteratorBO$new(),
opt_terminator = OptTerminatorSteps$new(5),
surrogate_model = SurrogateModelGPfit$new(design = design),
proposal_generator = ProposalGeneratorMultiCL$new(
acq_optimizer = AcqOptimizerGenSA$new(control = list(maxit = 100)),
acq_function = AcqFunctionCB$new(lambda = 2),
n = 2,
lie = "min"
)
)
res = bayopt(op)Combining multiple ProposalGeneratorSingle to a Multipoint Proposal.
acq_optimizer = AcqOptimizerGenSA$new(control = list(maxit = 100))
proposal_generators = lapply(c(1, 2, 4), function(x) {
ProposalGeneratorSingle$new(
acq_optimizer = acq_optimizer,
acq_function = AcqFunctionCB$new(lambda = x)
)
})
op = OptProblem$new(
target_fun = target_fun,
design = design,
opt_iterator = OptIteratorBO$new(),
opt_terminator = OptTerminatorSteps$new(5),
surrogate_model = SurrogateModelGPfit$new(design = design),
proposal_generator = ProposalGeneratorMultiEnsemble$new(
proposal_generator_singles = proposal_generators
)
)
res = bayopt(op)