estimateRuntimes(tab, ..., reg = getDefaultRegistry())

## S3 method for class 'RuntimeEstimate'
print(x, n = 1L, ...)

# Create a simple toy registry
set.seed(1)
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE, seed = 1)
addProblem(name = "iris", data = iris, fun = function(data, ...) nrow(data), reg = tmp)
addAlgorithm(name = "nrow", function(instance, ...) nrow(instance), reg = tmp)
addAlgorithm(name = "ncol", function(instance, ...) ncol(instance), reg = tmp)
addExperiments(algo.designs = list(nrow = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
addExperiments(algo.designs = list(ncol = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)

# We use the job parameters to predict runtimes
tab = unwrap(getJobPars(reg = tmp))

# First we need to submit some jobs so that the forest can train on some data.
# Thus, we just sample some jobs from the registry while grouping by factor variables.
library(data.table)
ids = tab[, .SD[sample(nrow(.SD), 5)], by = c("problem", "algorithm", "y")]
setkeyv(ids, "job.id")
submitJobs(ids, reg = tmp)
waitForJobs(reg = tmp)

# We "simulate" some more realistic runtimes here to demonstrate the functionality:
# - Algorithm "ncol" is 5 times more expensive than "nrow"
# - x has no effect on the runtime
# - If y is "a" or "b", the runtimes are really high
runtime = function(algorithm, x, y) {
  ifelse(algorithm == "nrow", 100L, 500L) + 1000L * (y %in% letters[1:2])
}
tmp$status[ids, done := done + tab[ids, runtime(algorithm, x, y)]]
rjoin(sjoin(tab, ids), getJobStatus(ids, reg = tmp)[, c("job.id", "time.running")])

# Estimate runtimes:
est = estimateRuntimes(tab, reg = tmp)
print(est)
rjoin(tab, est$runtimes)
print(est, n = 10)

# Submit jobs with longest runtime first:
ids = est$runtimes[type == "estimated"][order(runtime, decreasing = TRUE)]
print(ids)
## Not run: 
submitJobs(ids, reg = tmp)

## End(Not run)

# Group jobs into chunks with runtime < 1h
ids = est$runtimes[type == "estimated"]
ids[, chunk := binpack(runtime, 3600)]
print(ids)
print(ids[, list(runtime = sum(runtime)), by = chunk])
## Not run: 
submitJobs(ids, reg = tmp)

## End(Not run)

# Group jobs into 10 chunks with similar runtime
ids = est$runtimes[type == "estimated"]
ids[, chunk := lpt(runtime, 10)]
print(ids[, list(runtime = sum(runtime)), by = chunk])