Opportunity to reduce redundant evaluation #442
Description
In a simple example of a mixed space optimization with mostly-default parameters and a deterministic objective, mbo repeatedly evaluates the same point:
library(mlrMBO)
library(data.table)
fun = function(x) {
if(x$method == "a") return(x$number)
return(1)
}
space = makeParamSet(
makeDiscreteParam("method", values = c("a", "b")),
makeNumericParam("number", lower = 0,upper = 2 * pi, requires = quote(method == "a"))
)
obj = makeSingleObjectiveFunction(
name = "mixed_example",
fn = fun,
par.set = space,
has.simple.signature = FALSE,
noisy = FALSE,
minimize = TRUE
)
run = mbo(obj, control = makeMBOControl(), show.info = FALSE)
## ... many warnings of the form:
# Warning in generateDesign(control$infill.opt.focussearch.points, ps.local, :
# generateDesign could only produce 1 points instead of 1000!
DT = as.data.table(run$opt.path)
head(DT[is.na(number), c("method", "number", "y")])
The result shows that one point got evaluated repeatedly, even though I set noisy = FALSE in the objective. Such repeated evaluation is costly in other settings -- is there any reason to allow it?