Expected Improvement.
Dictionary
This AcqFunction can be instantiated via the dictionary
mlr_acqfunctions or with the associated sugar function acqf()
:
Parameters
"epsilon"
(numeric(1)
)
\(\epsilon\) value used to determine the amount of exploration. Higher values result in the importance of improvements predicted by the posterior mean decreasing relative to the importance of potential improvements in regions of high predictive uncertainty. Defaults to0
(standard Expected Improvement).
References
Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization of Expensive Black-Box Functions.” Journal of Global optimization, 13(4), 455–492.
Super classes
bbotk::Objective
-> mlr3mbo::AcqFunction
-> AcqFunctionEI
Public fields
y_best
(
numeric(1)
)
Best objective function value observed so far. In the case of maximization, this already includes the necessary change of sign.
Methods
Method new()
Creates a new instance of this R6 class.
Usage
AcqFunctionEI$new(surrogate = NULL, epsilon = 0)
Arguments
surrogate
(
NULL
| SurrogateLearner).epsilon
(
numeric(1)
).
Examples
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud")) {
library(bbotk)
library(paradox)
library(mlr3learners)
library(data.table)
fun = function(xs) {
list(y = xs$x ^ 2)
}
domain = ps(x = p_dbl(lower = -10, upper = 10))
codomain = ps(y = p_dbl(tags = "minimize"))
objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
instance = OptimInstanceBatchSingleCrit$new(
objective = objective,
terminator = trm("evals", n_evals = 5))
instance$eval_batch(data.table(x = c(-6, -5, 3, 9)))
learner = default_gp()
surrogate = srlrn(learner, archive = instance$archive)
acq_function = acqf("ei", surrogate = surrogate)
acq_function$surrogate$update()
acq_function$update()
acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}
#> acq_ei
#> <num>
#> 1: 4.092188
#> 2: 4.549039
#> 3: 5.037109