###################################################### -*- mode: r -*- #####
## Scenario setup for Iterated Race (irace).
############################################################################

## To use the default value of a parameter of iRace, simply do not set
## the parameter (comment it out in this file, and do not give any
## value on the command line).

## Parameter used to define the number of configurations sampled and evaluated
## at each iteration.
# mu = 2

## Seed of the random number generator (by default, generate a random seed).
# seed = NA

## Enable/disable the soft restart strategy that avoids premature convergence
## of the probabilistic model.
softRestart = 1

## Soft restart threshold value for numerical parameters. If NA, NULL or "",
## it is computed as 10^-digits.
softRestartThreshold = 1.0e-1

## If the target algorithm is deterministic, configurations will be evaluated
## only once per instance.
deterministic = 1

## Randomly sample the training instances or use them in the order given.
sampleInstances = 1

## Minimum number of configurations needed to continue the execution of each
## race (iteration).
minNbSurvival = 10

## File that contains the description of the parameters of the target
## algorithm.
# parameterFile = "./parameters.txt"

## Executable called for each configuration that executes the target algorithm
## to be tuned. See the templates and examples provided.
targetRunner = "./target-runner"

## File that contains a table of initial configurations. If empty or NULL, all
## initial configurations are randomly generated.
# configurationsFile = "./configurations.txt"

## File that contains a list of logical expressions that cannot be TRUE for
## any evaluated configuration. If empty or NULL, do not use forbidden
## expressions.
forbiddenFile = "./forbidden.txt"

## Number of elite configurations returned by irace that will be tested if
## test instances are provided.
# testNbElites = 10

## Number of instances evaluated before the first elimination test. It must be
## a multiple of eachTest.
# firstTest = 75

## Number of instances evaluated between elimination tests.
# eachTest = 75

## Maximum number of runs (invocations of targetRunner) that will be
## performed. It determines the maximum budget of experiments for the tuning.
# maxExperiments = 1600

## Maximum total execution time in seconds for the executions of targetRunner.
## targetRunner must return two values: cost and time.
# maxTime = 1440 * 40
# maxTime = 400

## Fraction (smaller than 1) of the budget used to estimate the mean
## computation time of a configuration. Only used when maxTime > 0
# budgetEstimation = 0.0002

## Number of calls to targetRunner to execute in parallel. Values 0 or 1 mean
## no parallelization.
# parallel = 8

## Directory where training instances are located; either absolute path or
## relative to current directory. If no trainInstancesFiles is provided, all
## the files in trainInstancesDir will be listed as instances.
# trainInstancesDir = "./train_instances"

## Directory where testing instances are located, either absolute or relative
## to current directory.
# testInstancesDir = "./test_instances"

## Directory where the programs will be run.
# execDir = "./"

## File to save tuning results as an R dataset, either absolute path or
## relative to execDir.
# logFile = "./irace.Rdata"

## File that contains a list of training instances and optionally additional
## parameters for them. If trainInstancesDir is provided, irace will search
## for the files in this folder.
# trainInstancesFile = ""

## File containing a list of test instances and optionally additional
## parameters for them.
# testInstancesFile = ""

## Enable/disable testing the elite configurations found at each iteration.
# testIterationElites = 0

## Statistical test used for elimination. The default value selects t-test if
## capping is enabled or F-test, otherwise. Valid values are: F-test (Friedman
## test), t-test (pairwise t-tests with no correction), t-test-bonferroni
## (t-test with Bonferroni's correction for multiple comparisons), t-test-holm
## (t-test with Holm's correction for multiple comparisons).
# testType = ""

## Executable that will be used to launch the target runner, when targetRunner
## cannot be executed directly (.e.g, a Python script in Windows).
# targetRunnerLauncher = ""

## Command-line arguments provided to targetRunnerLauncher. The substrings
## \{targetRunner\} and \{targetRunnerArgs\} will be replaced by the value of
## the option targetRunner and by the arguments usually passed when calling
## targetRunner, respectively. Example: "-m {targetRunner --args
## {targetRunnerArgs}"}.
# targetRunnerLauncherArgs = "{targetRunner} {targetRunnerArgs}"

## Number of times to retry a call to targetRunner if the call failed.
# targetRunnerRetries = 0

## Optional data passed to targetRunner. This is ignored by the default
## targetRunner function, but it may be used by custom targetRunner functions
## to pass persistent data around.
# targetRunnerData = ""

## Optional R function to provide custom parallelization of targetRunner.
# targetRunnerParallel = ""

## Optional script or R function that provides a numeric value for each
## configuration. See templates/target-evaluator.tmpl
# targetEvaluator = ""

## Minimum time unit that is still (significantly) measureable.
# minMeasurableTime = 0.01

## Enable/disable load-balancing when executing experiments in parallel.
## Load-balancing makes better use of computing resources, but increases
## communication overhead. If this overhead is large, disabling load-balancing
## may be faster.
# loadBalancing = 1

## Enable/disable MPI. Use Rmpi to execute targetRunner in parallel (parameter
## parallel is the number of slaves).
# mpi = 0

## Specify how irace waits for jobs to finish when targetRunner submits jobs
## to a batch cluster: sge, pbs, torque, slurm or htcondor. targetRunner must
## submit jobs to the cluster using, for example, qsub.
# batchmode = 0

## Maximum number of decimal places that are significant for numerical (real)
## parameters.
# digits = 4

## Reduce the output generated by irace to a minimum.
# quiet = 0

## Debug level of the output of irace. Set this to 0 to silence all debug
## messages. Higher values provide more verbose debug messages.
# debugLevel = 0

## Enable/disable elitist irace.
# elitist = 1

## Number of instances added to the execution list before previous instances
## in elitist irace.
# elitistNewInstances = 1

## In elitist irace, maximum number per race of elimination tests that do not
## eliminate a configuration. Use 0 for no limit.
# elitistLimit = 2

## User-defined R function that takes a configuration generated by irace and
## repairs it.
# repairConfiguration = ""

## Enable the use of adaptive capping, a technique designed for minimizing the
## computation time of configurations. This is only available when elitist is
## active.
# capping = 0

## Measure used to obtain the execution bound from the performance of the
## elite configurations: median, mean, worst, best.
# cappingType = "median"

## Method to calculate the mean performance of elite configurations: candidate
## or instance.
# boundType = "candidate"

## Maximum execution bound for targetRunner. It must be specified when capping
## is enabled.
# boundMax = 0

## Precision used for calculating the execution time. It must be specified
## when capping is enabled.
# boundDigits = 0

## Penalization constant for timed out executions (executions that reach
## boundMax execution time).
# boundPar = 1

## Replace the configuration cost of bounded executions with boundMax.
# boundAsTimeout = 1

## Percentage of the configuration budget used to perform a postselection race
## of the best configurations of each iteration after the execution of irace.
# postselection = 0

## Enable/disable AClib mode. This option enables compatibility with
## GenericWrapper4AC as targetRunner script.
# aclib = 0

## Maximum number of iterations.
# nbIterations = 0

## Number of runs of the target algorithm per iteration.
# nbExperimentsPerIteration = 0

## Number of configurations to be sampled and evaluated at each iteration.
# nbConfigurations = 0

## Confidence level for the elimination test.
# confidence = 0.95

## END of scenario file
############################################################################

