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Fix #201: focussearch now handles discrete vector parameters (cont.) #257

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98 changes: 78 additions & 20 deletions R/infillOptFocus.R
Original file line number Diff line number Diff line change
Expand Up @@ -8,11 +8,40 @@
# See infillOptCMAES.R for interface explanation.
infillOptFocus = function(infill.crit, models, control, par.set, opt.path, design, iter, ...) {
global.y = Inf

discrete.vector.pars = filterParams(par.set, type = c("discretevector", "logicalvector"))

all.requirements = extractSubList(par.set$pars, "requires", simplify = FALSE)
all.requirement.vars = unique(unlist(lapply(all.requirements, all.vars)))
if (any(all.requirement.vars %in% getParamIds(discrete.vector.pars))) {
stop("Cannot do focus search when some variables have requirements that depend on discrete or logical vector parameters.")
}


# restart the whole crap some times
# perform multiple starts
for (restart.iter in seq_len(control$infill.opt.restarts)) {
# copy parset so we can shrink it
ps.local = par.set

# Handle discrete vectors (and logical vectors):
# The problem is that for discrete vectors, we can't adjust the range dimension-wise.
# Instead we store the range of each discrete vectorparameter dimension in the list of named characters
# `discrete.vector.mapping`. In each iteration a random value (that does not contain
# the optimum) is dropped from each vector on this list. The $values of the parameters in the parameterset also
# need to be modified to reflect the reduced range: from them, always the last value is dropped.
# Then `discrete.vector.mapping` is a mapping that maps, for each discrete vector param dimension
# with originally n values, from the sampled value (levels 1 to n - #(dropped levels)) to the acutal levels with
# random dropouts.
#
# Since the requirements of the param set are queried while generating the design, this breaks if
# there are requirements depending on discrete vector parameters.
discrete.vector.mapping = lapply(discrete.vector.pars$pars,
function(param) rep(list(setNames(names(param$values), names(param$values))), param$len))
discrete.vector.mapping = unlist(discrete.vector.mapping, recursive=FALSE)
if (!isEmpty(discrete.vector.pars)) {
names(discrete.vector.mapping) = getParamIds(discrete.vector.pars, with.nr = TRUE, repeated = TRUE)
}


# do iterations where we focus the region-of-interest around the current best point
for (local.iter in seq_len(control$infill.opt.focussearch.maxit)) {
Expand All @@ -21,13 +50,20 @@ infillOptFocus = function(infill.crit, models, control, par.set, opt.path, desig

# convert to param encoding our model was trained on and can use
newdesign = convertDataFrameCols(newdesign, ints.as.num = TRUE, logicals.as.factor = TRUE)
y = infill.crit(newdesign, models, control, ps.local, design, iter, ...)

# handle discrete vectors
for (dfindex in names(discrete.vector.mapping)) {
mapping = discrete.vector.mapping[[dfindex]]
levels(newdesign[[dfindex]]) = mapping[levels(newdesign[[dfindex]])]
}

y = infill.crit(newdesign, models, control, par.set, design, iter, ...)

# get current best value
local.index = getMinIndex(y, ties.method = "random")
local.y = y[local.index]
local.x.df = newdesign[local.index, , drop = FALSE]
local.x.list = dfRowToList(recodeTypes(local.x.df, ps.local), ps.local, 1)
local.x.list = dfRowToList(recodeTypes(local.x.df, par.set), par.set, 1)

# if we found a new best value, store it
if (local.y < global.y) {
Expand All @@ -39,24 +75,46 @@ infillOptFocus = function(infill.crit, models, control, par.set, opt.path, desig
ps.local$pars = lapply(ps.local$pars, function(par) {
# only shrink when there is a value
val = local.x.list[[par$id]]
if (!isScalarNA(val)) {
if (isNumeric(par)) {
# shrink to range / 2, centered at val
range = par$upper - par$lower
par$lower = pmax(par$lower, val - (range / 4))
par$upper = pmin(par$upper, val + (range / 4))
if (isInteger(par)) {
par$lower = floor(par$lower)
par$upper = ceiling(par$upper)
if (isScalarNA(val)) {
return(par)
}
if (isNumeric(par)) {
# shrink to range / 2, centered at val
range = par$upper - par$lower
par$lower = pmax(par$lower, val - (range / 4))
par$upper = pmin(par$upper, val + (range / 4))
if (isInteger(par)) {
par$lower = floor(par$lower)
par$upper = ceiling(par$upper)
}
} else if (isDiscrete(par)) {
# randomly drop a level, which is not val
if (length(par$values) <= 1L) {
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== 0

return(par)
}
# need to do some magic to handle discrete vectors
if (par$type %nin% c("discretevector", "logicalvector")) {
val.names = names(par$values)
# remove current val from delete options, should work also for NA
val.names = val.names[!sapply(par$values, identical, y=val)] # remember, 'val' can be any type
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vlapply

to.del = sample(val.names, 1)
par$values[[to.del]] = NULL
} else {
# we remove the last element of par$values and a random element for
# each dimension in discrete.vector.mapping.
par$values = par$values[-length(par$values)]
if (par$type != "logicalvector") {
# for discretevectorparam val would be a list; convert to character vector
val = names(val)
}
} else if (isDiscrete(par)) {
# randomly drop a level, which is not val
if (length(par$values) > 1L) {
val.names = names(par$values)
# remove current val from delete options, should work also for NA
val.names = setdiff(val.names, val)
to.del = sample(seq_along(val.names), 1)
par$values = par$values[-to.del]
for (dimnum in seq_len(par$len)) {
dfindex = paste0(par$id, dimnum)
newmap = val.names = discrete.vector.mapping[[dfindex]]
val.names = val.names[val.names != val[dimnum]]
to.del = sample(val.names, 1)
newmap = newmap[newmap != to.del]
names(newmap) = names(par$values)
discrete.vector.mapping[[dfindex]] <<- newmap
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<<-?

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we need the updated discrete.vector.mapping outside of the lapply

}
}
}
Expand Down
5 changes: 5 additions & 0 deletions tests/testthat/test_infill_opt_focus.R
Original file line number Diff line number Diff line change
Expand Up @@ -68,13 +68,18 @@ test_that("complex param space, dependencies, focusing, restarts", {
if(x$disc2 == 'a') tmp3 = log(x$realA) + x$intA^4 + ifelse(x$discA == 'm', 5, 0)
if(x$disc2 == 'b') tmp3 = exp(x$realB) + ifelse(x$discB == 'R', sin(x$realBR), sin(x$realBNR))
if(x$disc2 == "c") tmp3 = 500
assert(is.list(x$discVec))
assert(x$discVec[[1]] %in% c("a", "b", "c"))
assert(x$discScal %in% c("x", "y", "z"))
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This is not what assert is for. Use expect_is/expect_true and expect_subset

tmp1 + tmp2 + tmp3
},
par.set = makeParamSet(
makeNumericParam("real1", lower = 0, upper = 1000),
makeIntegerParam("int1", lower = -100, upper = 100),
makeNumericVectorParam("realVec", len = 10, lower = -50, upper = 50),
makeIntegerVectorParam("intVec", len = 3, lower = 0, upper = 100),
makeDiscreteVectorParam("discVec", len = 3, c(x = "a", y = "b", z = "c")),
makeDiscreteParam("discScal", c(a = "x", b = "y", c = "z")),
makeNumericParam("real2", lower = -1, upper = 1),
makeDiscreteParam("disc1", values = c("foo", "bar"), requires = quote(real2 < 0)),
makeNumericParam("real3", lower = -100, upper = 100, requires = quote(real2 > 0)),
Expand Down