defaultScenario {irace} | R Documentation |
Default scenario settings
Description
Return scenario object with default values.
Usage
defaultScenario(scenario = list(), params_def = .irace.params.def)
Arguments
scenario |
( |
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 as10^-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 orNULL
, 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 optiontargetRunner
and by the arguments usually passed when callingtargetRunner
, 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 defaulttargetRunner
function, but it may be used by customtargetRunner
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. Values0
or1
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 executetargetRunner
in parallel (parameterparallel
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 intrainInstancesDir
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
ifcapping
is enabled orF-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.
median: Median 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.
candidate: Mean execution times across the executed instances and the current one.
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 orNULL
, 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
)
Author(s)
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.