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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estimateRuntimes.R
\name{estimateRuntimes}
\alias{estimateRuntimes}
\alias{print.RuntimeEstimate}
\title{Estimate Remaining Runtimes}
\usage{
estimateRuntimes(tab, ..., reg = getDefaultRegistry())
\method{print}{RuntimeEstimate}(x, n = 1L, ...)
}
\arguments{
\item{tab}{[\code{\link{data.table}}]\cr
Table with column \dQuote{job.id} and additional columns to predict the runtime.
Observed runtimes will be looked up in the registry and serve as dependent variable.
All columns in \code{tab} except \dQuote{job.id} will be passed to \code{\link[ranger]{ranger}} as
independent variables to fit the model.}
\item{...}{[ANY]\cr
Additional parameters passed to \code{\link[ranger]{ranger}}. Ignored for the \code{print} method.}
\item{reg}{[\code{\link{Registry}}]\cr
Registry. If not explicitly passed, uses the default registry (see \code{\link{setDefaultRegistry}}).}
\item{x}{[\code{RuntimeEstimate}]\cr
Object to print.}
\item{n}{[\code{integer(1)}]\cr
Number of parallel jobs to assume for runtime estimation.}
}
\value{
[\code{RuntimeEstimate}] which is a \code{list} with two named elements:
\dQuote{runtimes} is a \code{\link{data.table}} with columns \dQuote{job.id},
\dQuote{runtime} (in seconds) and \dQuote{type} (\dQuote{estimated} if runtime is estimated,
\dQuote{observed} if runtime was observed).
The other element of the list named \dQuote{model}] contains the fitted random forest object.
}
\description{
Estimates the runtimes of jobs using the random forest implemented in \pkg{ranger}.
Observed runtimes are retrieved from the \code{\link{Registry}} and runtimes are
predicted for unfinished jobs.
The estimated remaining time is calculated in the \code{print} method.
You may also pass \code{n} here to determine the number of parallel jobs which is then used
in a simple Longest Processing Time (LPT) algorithm to give an estimate for the parallel runtime.
}
\examples{
\dontshow{ batchtools:::example_push_temp(1) }
# 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$runtimes[type == "estimated"][order(runtime, decreasing = TRUE)]
print(ids)
\dontrun{
submitJobs(ids, reg = tmp)
}
# Group jobs into chunks with runtime < 1h
ids = est$runtimes[type == "estimated"]
ids[, chunk := binpack(runtime, 3600)]
print(ids)
print(ids[, list(runtime = sum(runtime)), by = chunk])
\dontrun{
submitJobs(ids, reg = tmp)
}
# 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])
}
\seealso{
\code{\link{binpack}} and \code{\link{lpt}} to chunk jobs according to their estimated runtimes.
}
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