###################################################### -*- 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).

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

## 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/irace.Rdata"

## Previously saved log file to recover the execution of irace, either
## absolute path or relative to the current directory.  If empty or NULL,
## recovery is not performed.
# recoveryFile = ""

## 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 = "~/irace/"

## 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 = train_instances.txt
trainInstancesFile= "instances-list.txt"

## File that contains a set 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 = ""

## Script called for each configuration that executes the target algorithm
## to be tuned. See templates.
targetRunner = "~/irace/target-runner"

## 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 = ""

## Maximum number of runs (invocations of targetRunner) that will be
## performed. It determines the maximum budget of experiments for the
## tuning.
#maxExperiments = 60
#maxExperiments = 2000

## Maximum total execution time in seconds for the executions of
## targetRunner. targetRunner must return two values: cost and time.
#432000=5dias
#maxTime = 432000
maxTime = 4000
#maxTime = 18000

## Fraction (smaller than 1) of the budget used to estimate the mean
## computation time of a configuration. Only used when maxTime > 0
# budgetEstimation = 0.02

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

## Debug level of the output of irace. Set this to 0 to silence all debug
## messages. Higher values provide more verbose debug messages.
debugLevel = 2

## Number of iterations.
# nbIterations = 0

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

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

## Statistical test used for elimination. Default test is always F-test
## unless capping is enabled, in which case the default test is t-test.
## 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 = "F-test"

## Number of instances evaluated before the first elimination test. It
## must be a multiple of eachTest.
# firstTest = 5
 firstTest = 2

## Number of instances evaluated between elimination tests.
# eachTest = 1

## Minimum number of configurations needed to continue the execution of
## each race (iteration).
# minNbSurvival = 0

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

## Parameter used to define the number of configurations sampled and
## evaluated at each iteration.
# mu = 5

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

## If the target algorithm is deterministic, configurations will be
## evaluated only once per instance.
# deterministic = 0

## Seed of the random number generator (by default, generate a random
## seed).
seed = 2020

## Number of calls to targetRunner to execute in parallel. Values 0 or 1
## mean no parallelization.
parallel = 4

## 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 or slurm. targetRunner must
## submit jobs to the cluster using, for example, qsub.
# batchmode = 0

## 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 = ""

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

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

## Number of elite configurations returned by irace that will be tested if
## test instances are provided.
# testNbElites = 1

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

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