Probability of Improvement.
Dictionary
This AcqFunction can be instantiated via the dictionary
mlr_acqfunctions or with the associated sugar function acqf()
:
References
Kushner, J. H (1964). “A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise.” Journal of Basic Engineering, 86(1), 97–106.
Super classes
bbotk::Objective
-> mlr3mbo::AcqFunction
-> AcqFunctionPI
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
AcqFunctionPI$new(surrogate = NULL)
Arguments
surrogate
(
NULL
| SurrogateLearner).
Examples
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud")) {
library(bbotk)
library(paradox)
library(mlr3learners)
library(data.table)
fun = function(xs) {
list(y = xs$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("pi", surrogate = surrogate)
acq_function$surrogate$update()
acq_function$update()
acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}
#> acq_pi
#> <num>
#> 1: 0.2666736
#> 2: 0.2939486
#> 3: 0.3509359