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

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

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

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

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

## 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 = ""

## 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 = ""

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

## 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

## 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 = ""

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

## Number of training instances, that make up a 'block' in
## `trainInstancesFile`. Elimination of configurations will only be performed
## after evaluating a complete block and never in the middle of a block. Each
## block typically contains one instance from each instance class (type or
## family) and the block size is the number of classes. The value of
## `blockSize` will multiply `firstTest`, `eachTest` and
## `elitistNewInstances`.
# blockSize = 1

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

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

## 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 `targetRunner` (or
## `targetRunnerLauncher` if defined). The substrings `{configurationID}`,
## `{instanceID}`, `{seed}`, `{instance}`, and `{bound}` will be replaced by
## their corresponding values. The substring `{targetRunnerArgs}` will be
## replaced by the concatenation of the switch and value of all active
## parameters of the particular configuration being evaluated.  The substring
## `{targetRunner}`, if present, will be replaced by the value of
## `targetRunner` (useful when using `targetRunnerLauncher`).
# targetCmdline = "{configurationID} {instanceID} {seed} {instance} {bound} {targetRunnerArgs}"

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

## Timeout in seconds of any `targetRunner` call (only applies to
## `target-runner` executables not to R functions), ignored if 0.
# targetRunnerTimeout = 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 = ""

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

## Maximum number of runs (invocations of `targetRunner`) that will be
## performed. It determines the maximum budget of experiments for the tuning.
# maxExperiments = 0

## Minimum number of runs (invocations of `targetRunner`) that will be
## performed. It determines the minimum budget of experiments for the tuning.
## The actual budget depends on the number of parameters and `minSurvival`.
# minExperiments = NA

## Maximum total execution time for the executions of `targetRunner`.
## `targetRunner` must return two values: cost and time. This value and the
## one returned by `targetRunner` must use the same units (seconds, minutes,
## iterations, evaluations, ...).
# maxTime = 0

## Fraction (smaller than 1) of the budget used to estimate the mean
## computation time of a configuration. Only used when `maxTime` > 0
# budgetEstimation = 0.05

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

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

## 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

## 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

## 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.
# softRestartThreshold = 1e-04

## 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. Capping is enabled by default if
## `elitist` is active, `maxTime > 0` and `boundMax > 0`.
# capping = NA

## If set to 1, elimination due to capping only happens after `firstTest`
## instances are seen.
# cappingAfterFirstTest = 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

## Perform a postselection race after the execution of irace to consume all
## remaining budget. Value 0 disables the postselection race.
# postselection = 1

## 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

## 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

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