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irace (version 3.5)

defaultScenario: Default scenario settings

Description

Return scenario object with default values.

Usage

defaultScenario(scenario = list(), params_def = .irace.params.def)

Value

A list indexed by the irace parameter names, containing the default values for each parameter, except for those already present in the scenario passed as argument. The scenario list contains the following elements:

  • General options:

    scenarioFile

    Path of the file that describes the configuration scenario setup and other irace settings. (Default: "./scenario.txt")

    execDir

    Directory where the programs will be run. (Default: "./")

    logFile

    File to save tuning results as an R dataset, either absolute path or relative to execDir. (Default: "./irace.Rdata")

    quiet

    Reduce the output generated by irace to a minimum. (Default: 0)

    debugLevel

    Debug level of the output of irace. Set this to 0 to silence all debug messages. Higher values provide more verbose debug messages. (Default: 0)

    seed

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

    repairConfiguration

    User-defined R function that takes a configuration generated by irace and repairs it. (Default: "")

    postselection

    Percentage of the configuration budget used to perform a postselection race of the best configurations of each iteration after the execution of irace. (Default: 0)

    aclib

    Enable/disable AClib mode. This option enables compatibility with GenericWrapper4AC as targetRunner script. (Default: 0)

  • Elitist irace:

    elitist

    Enable/disable elitist irace. (Default: 1)

    elitistNewInstances

    Number of instances added to the execution list before previous instances in elitist irace. (Default: 1)

    elitistLimit

    In elitist irace, maximum number per race of elimination tests that do not eliminate a configuration. Use 0 for no limit. (Default: 2)

  • Internal irace options:

    sampleInstances

    Randomly sample the training instances or use them in the order given. (Default: 1)

    softRestart

    Enable/disable the soft restart strategy that avoids premature convergence of the probabilistic model. (Default: 1)

    softRestartThreshold

    Soft restart threshold value for numerical parameters. If NA, NULL or "", it is computed as 10^-digits. (Default: "")

    nbIterations

    Maximum number of iterations. (Default: 0)

    nbExperimentsPerIteration

    Number of runs of the target algorithm per iteration. (Default: 0)

    minNbSurvival

    Minimum number of configurations needed to continue the execution of each race (iteration). (Default: 0)

    nbConfigurations

    Number of configurations to be sampled and evaluated at each iteration. (Default: 0)

    mu

    Parameter used to define the number of configurations sampled and evaluated at each iteration. (Default: 5)

  • Target algorithm parameters:

    parameterFile

    File that contains the description of the parameters of the target algorithm. (Default: "./parameters.txt")

    forbiddenExps

    Vector of R logical expressions that cannot evaluate to TRUE for any evaluated configuration. (Default: "")

    forbiddenFile

    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. (Default: "")

    digits

    Maximum number of decimal places that are significant for numerical (real) parameters. (Default: 4)

  • Target algorithm execution:

    targetRunner

    Executable called for each configuration that executes the target algorithm to be tuned. See the templates and examples provided. (Default: "./target-runner")

    targetRunnerLauncher

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

    targetRunnerLauncherArgs

    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}". (Default: "{targetRunner} {targetRunnerArgs}")

    targetRunnerRetries

    Number of times to retry a call to targetRunner if the call failed. (Default: 0)

    targetRunnerData

    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. (Default: "")

    targetRunnerParallel

    Optional R function to provide custom parallelization of targetRunner. (Default: "")

    targetEvaluator

    Optional script or R function that provides a numeric value for each configuration. See templates/target-evaluator.tmpl (Default: "")

    deterministic

    If the target algorithm is deterministic, configurations will be evaluated only once per instance. (Default: 0)

    parallel

    Number of calls to targetRunner to execute in parallel. Values 0 or 1 mean no parallelization. (Default: 0)

    loadBalancing

    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. (Default: 1)

    mpi

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

    batchmode

    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. (Default: 0)

  • Initial configurations:

    initConfigurations

    Data frame describing initial configurations (usually read from a file using readConfigurations). (Default: "")

    configurationsFile

    File that contains a table of initial configurations. If empty or NULL, all initial configurations are randomly generated. (Default: "")

  • Training instances:

    instances

    Character vector of the instances to be used in the targetRunner. (Default: "")

    trainInstancesDir

    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. (Default: "./Instances")

    trainInstancesFile

    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. (Default: "")

  • Tuning budget:

    maxExperiments

    Maximum number of runs (invocations of targetRunner) that will be performed. It determines the maximum budget of experiments for the tuning. (Default: 0)

    maxTime

    Maximum total execution time in seconds for the executions of targetRunner. targetRunner must return two values: cost and time. (Default: 0)

    budgetEstimation

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

    minMeasurableTime

    Minimum time unit that is still (significantly) measureable. (Default: 0.01)

  • Statistical test:

    testType

    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). (Default: "")

    firstTest

    Number of instances evaluated before the first elimination test. It must be a multiple of eachTest. (Default: 5)

    eachTest

    Number of instances evaluated between elimination tests. (Default: 1)

    confidence

    Confidence level for the elimination test. (Default: 0.95)

  • Adaptive capping:

    capping

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

    cappingType

    Measure used to obtain the execution bound from the performance of the elite configurations.

    • mean: Mean performance of the elite configurations.

    • best: Best performance of the elite configurations.

    • worst: Worst performance of the elite configurations.

    (Default: "median")

    boundType

    Method to calculate the mean performance of elite configurations.

    • instance: Execution time of the current instance.

    (Default: "candidate")

    boundMax

    Maximum execution bound for targetRunner. It must be specified when capping is enabled. (Default: 0)

    boundDigits

    Precision used for calculating the execution time. It must be specified when capping is enabled. (Default: 0)

    boundPar

    Penalization constant for timed out executions (executions that reach boundMax execution time). (Default: 1)

    boundAsTimeout

    Replace the configuration cost of bounded executions with boundMax. (Default: 1)

  • Recovery:

    recoveryFile

    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. (Default: "")

  • Testing:

    testInstancesDir

    Directory where testing instances are located, either absolute or relative to current directory. (Default: "")

    testInstancesFile

    File containing a list of test instances and optionally additional parameters for them. (Default: "")

    testInstances

    Character vector of the instances to be used in the targetRunner when executing the testing. (Default: "")

    testNbElites

    Number of elite configurations returned by irace that will be tested if test instances are provided. (Default: 1)

    testIterationElites

    Enable/disable testing the elite configurations found at each iteration. (Default: 0)

Arguments

scenario

(list())
Data structure containing irace settings. The data structure has to be the one returned by the function defaultScenario() or readScenario().

params_def

(data.frame())
Definition of the options accepted by the scenario. This should only be modified by packages that wish to extend irace.

Author

Manuel López-Ibáñez and Jérémie Dubois-Lacoste

See Also

readScenario()

for reading a configuration scenario from a file.

printScenario()

prints the given scenario.

defaultScenario()

returns the default scenario settings of irace.

checkScenario()

to check that the scenario is valid.