Nothing
parscores <-
function(data=NULL, model=NULL, factor=10, timeout=NULL, addCosts=NULL) {
if(is.null(data) || is.null(model)) {
stop("Need both data and model to calculate PAR scores!")
}
if(is.null(data$success)) {
stop("Need successes to calculate PAR scores!")
}
if(is.null(addCosts)) {
ac = attr(model, "addCosts")
if(is.null(ac) || ac == TRUE) {
addCosts = TRUE
} else {
addCosts = FALSE
}
}
hp = attr(model, "hasPredictions")
if(is.null(hp) || hp != TRUE) {
if(length(data$test) > 0) {
predictions = rbind.fill(lapply(data$test, function(x) {
data$data = data$data[x,]
data$best = data$best[x]
model(data)
}))
} else {
predictions = model(data)
}
} else {
predictions = model$predictions
}
if(is.null(timeout)) {
# if timeout value wasn't given, assume maximum from data set
timeout = max(data$data[data$performance])
}
if(addCosts || !is.null(data$costs)) {
if(is.null(data$costGroups)) {
usedFeatures = intersect(data$cost, sapply(data$features, function(x) { paste(x, "cost", sep="_") }))
} else {
usedFeatures = subset(data$cost, sapply(data$cost, function(x) { length(intersect(data$costGroups[[x]], data$features)) > 0 }))
}
}
if(is.null(data$algorithmFeatures)) {
perfs = data$data[data$performance]
successes = data$data[data$success]
} else {
d = data$data[c(data$ids, data$algos, data$performance)]
perfs = convertLongToWide(data=d, timevar=data$algos, idvar=data$ids, prefix=paste(data$performance,".",sep=""))
perfs = perfs[data$algorithmNames]
d = data$data[c(data$ids, data$algos, data$success)]
successes = convertLongToWide(data=d, timevar=data$algos, idvar=data$ids, prefix=paste(data$success,".",sep=""))
successes = successes[data$algorithmNames]
colnames(successes) = paste(colnames(successes), data$success, sep="_")
}
if(!addCosts || is.null(data$cost)) {
costs = rep.int(0, nrow(perfs))
} else {
costs = apply(data$data[usedFeatures], 1, sum)
}
if(is.null(data$algorithmFeatures)) {
predictions$iid = match(do.call(paste, predictions[data$ids]), do.call(paste, data$data[data$ids]))
predictions$pid = match(predictions$algorithm, data$performance)
} else {
d = data$data[c(data$ids, data$algos, data$performance)]
d = reshape(d, direction = "wide", timevar = data$algos, idvar = data$ids)
colnames(d) = gsub(paste(data$performance,".",sep=""), "", colnames(d))
predictions$iid = match(do.call(paste, predictions[data$ids]), do.call(paste, d[data$ids]))
predictions$pid = match(predictions$algorithm, data$algorithmNames)
}
predictions$score = apply(predictions, 1, function(x) {
pid = as.numeric(x[["pid"]])
if(is.na(pid)) {
timeout * factor
} else {
iid = as.numeric(x[["iid"]])
score = as.numeric(perfs[iid,pid]) + costs[iid]
if(!as.logical(successes[iid,pid]) || score > timeout) {
score = timeout * factor
}
score
}
})
agg = aggregate(as.formula(paste("score~", paste(c(data$ids, "iteration"), sep="+", collapse="+"))), predictions, function(ss) { ss[1] })
agg$score
}
class(parscores) = "llama.metric"
attr(parscores, "minimize") = TRUE
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