227 lines
7.6 KiB
R
227 lines
7.6 KiB
R
###################################################### -*- mode: r -*- #####
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## Scenario setup for Iterated Race (irace).
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############################################################################
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## To use the default value of a parameter of iRace, simply do not set
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## the parameter (comment it out in this file, and do not give any
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## value on the command line).
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## File that contains the description of the parameters of the target
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## algorithm.
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parameterFile = "./fastga.param"
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## Directory where the programs will be run.
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execDir = "."
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## File to save tuning results as an R dataset, either absolute path or
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## relative to execDir.
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# logFile = "./irace.Rdata"
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## Previously saved log file to recover the execution of irace, either
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## absolute path or relative to the current directory. If empty or NULL,
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## recovery is not performed.
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# recoveryFile = ""
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## Directory where training instances are located; either absolute path or
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## relative to current directory. If no trainInstancesFiles is provided,
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## all the files in trainInstancesDir will be listed as instances.
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trainInstancesDir = "."
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## File that contains a list of training instances and optionally
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## additional parameters for them. If trainInstancesDir is provided, irace
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## will search for the files in this folder.
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trainInstancesFile = "./default.instances"
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## File that contains a table of initial configurations. If empty or NULL,
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## all initial configurations are randomly generated.
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# configurationsFile = ""
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## File that contains a list of logical expressions that cannot be TRUE
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## for any evaluated configuration. If empty or NULL, do not use forbidden
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## expressions.
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# forbiddenFile = ""
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## Script called for each configuration that executes the target algorithm
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## to be tuned. See templates.
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targetRunner = "./target-runner"
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## Number of times to retry a call to targetRunner if the call failed.
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# targetRunnerRetries = 0
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## Optional data passed to targetRunner. This is ignored by the default
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## targetRunner function, but it may be used by custom targetRunner
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## functions to pass persistent data around.
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# targetRunnerData = ""
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## Optional R function to provide custom parallelization of targetRunner.
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# targetRunnerParallel = ""
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## Optional script or R function that provides a numeric value for each
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## configuration. See templates/target-evaluator.tmpl
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# targetEvaluator = ""
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## Maximum number of runs (invocations of targetRunner) that will be
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## performed. It determines the maximum budget of experiments for the
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## tuning.
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maxExperiments = 2000
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## Maximum total execution time in seconds for the executions of
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## targetRunner. targetRunner must return two values: cost and time.
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# maxTime = 60
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## Fraction (smaller than 1) of the budget used to estimate the mean
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## computation time of a configuration. Only used when maxTime > 0
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# budgetEstimation = 0.02
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## Maximum number of decimal places that are significant for numerical
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## (real) parameters.
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digits = 2
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## Debug level of the output of irace. Set this to 0 to silence all debug
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## messages. Higher values provide more verbose debug messages.
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# debugLevel = 0
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## Number of iterations.
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# nbIterations = 0
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## Number of runs of the target algorithm per iteration.
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# nbExperimentsPerIteration = 0
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## Randomly sample the training instances or use them in the order given.
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# sampleInstances = 1
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## Statistical test used for elimination. Default test is always F-test
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## unless capping is enabled, in which case the default test is t-test.
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## Valid values are: F-test (Friedman test), t-test (pairwise t-tests with
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## no correction), t-test-bonferroni (t-test with Bonferroni's correction
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## for multiple comparisons), t-test-holm (t-test with Holm's correction
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## for multiple comparisons).
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# testType = "F-test"
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## Number of instances evaluated before the first elimination test. It
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## must be a multiple of eachTest.
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# firstTest = 5
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## Number of instances evaluated between elimination tests.
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# eachTest = 1
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## Minimum number of configurations needed to continue the execution of
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## each race (iteration).
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# minNbSurvival = 0
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## Number of configurations to be sampled and evaluated at each iteration.
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# nbConfigurations = 0
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## Parameter used to define the number of configurations sampled and
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## evaluated at each iteration.
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# mu = 5
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## Confidence level for the elimination test.
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# confidence = 0.95
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## If the target algorithm is deterministic, configurations will be
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## evaluated only once per instance.
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# deterministic = 0
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## Seed of the random number generator (by default, generate a random
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## seed).
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# seed = NA
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## Number of calls to targetRunner to execute in parallel. Values 0 or 1
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## mean no parallelization.
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# parallel = 0
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## Enable/disable load-balancing when executing experiments in parallel.
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## Load-balancing makes better use of computing resources, but increases
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## communication overhead. If this overhead is large, disabling
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## load-balancing may be faster.
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# loadBalancing = 1
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## Enable/disable MPI. Use Rmpi to execute targetRunner in parallel
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## (parameter parallel is the number of slaves).
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# mpi = 0
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## Specify how irace waits for jobs to finish when targetRunner submits
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## jobs to a batch cluster: sge, pbs, torque or slurm. targetRunner must
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## submit jobs to the cluster using, for example, qsub.
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# batchmode = 0
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## Enable/disable the soft restart strategy that avoids premature
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## convergence of the probabilistic model.
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# softRestart = 1
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## Soft restart threshold value for numerical parameters. If NA, NULL or
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## "", it is computed as 10^-digits.
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# softRestartThreshold = ""
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## Directory where testing instances are located, either absolute or
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## relative to current directory.
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# testInstancesDir = ""
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## File containing a list of test instances and optionally additional
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## parameters for them.
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# testInstancesFile = ""
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## Number of elite configurations returned by irace that will be tested if
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## test instances are provided.
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# testNbElites = 1
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## Enable/disable testing the elite configurations found at each
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## iteration.
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# testIterationElites = 0
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## Enable/disable elitist irace.
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# elitist = 1
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## Number of instances added to the execution list before previous
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## instances in elitist irace.
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# elitistNewInstances = 1
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## In elitist irace, maximum number per race of elimination tests that do
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## not eliminate a configuration. Use 0 for no limit.
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# elitistLimit = 2
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## User-defined R function that takes a configuration generated by irace
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## and repairs it.
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# repairConfiguration = ""
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## Enable the use of adaptive capping, a technique designed for minimizing
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## the computation time of configurations. This is only available when
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## elitist is active.
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# capping = 0
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## Measure used to obtain the execution bound from the performance of the
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## elite configurations: median, mean, worst, best.
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# cappingType = "median"
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## Method to calculate the mean performance of elite configurations:
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## candidate or instance.
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# boundType = "candidate"
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## Maximum execution bound for targetRunner. It must be specified when
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## capping is enabled.
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# boundMax = 0
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## Precision used for calculating the execution time. It must be specified
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## when capping is enabled.
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# boundDigits = 0
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## Penalization constant for timed out executions (executions that reach
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## boundMax execution time).
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# boundPar = 1
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## Replace the configuration cost of bounded executions with boundMax.
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# boundAsTimeout = 1
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## Percentage of the configuration budget used to perform a postselection
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## race of the best configurations of each iteration after the execution
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## of irace.
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# postselection = 0
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## Enable/disable AClib mode. This option enables compatibility with
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## GenericWrapper4AC as targetRunner script.
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# aclib = 0
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## END of scenario file
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############################################################################
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