Add experimental scripts for irace/fastga

This commit is contained in:
Alix ZHENG 2021-08-30 09:44:06 +02:00
commit 6febf4cceb
22 changed files with 1209 additions and 0 deletions

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#!/bin/bash
ldata=$1
file_py=$2
csvdir="csv_FA"
ldir=$(echo $(ls ${ldata}))
for data in ${ldir[@]} ; do
path="${ldata}/${data}"
cmd="python3 ${file_py} ${path}"
plan_name=$(echo ${data} | sed "s/data//")
mexp=$(echo ${data[@]} | cut -d _ -f2)
mevals=$(echo ${data[@]} | cut -d _ -f3)
ddate=$(echo ${data[@]} | cut -d _ -f4)
name="results_irace_plan${plan_name[@]:0:1}_${mexp}_${mevals}_${ddate}"
mkdir -p "${csvdir}/csv_plan${plan_name[@]:0:1}"
${cmd} > "${csvdir}/csv_plan${plan_name[@]:0:1}/${name}.csv"
done

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#!/bin/bash
ldata=$1 # eg : ./csv_plan2/ don t forget to end the path with /
file_py=$2
ldir=$(echo $(ls ${ldata}))
fastga_dir="fastga_results_all"
mkdir -p /scratchbeta/${USER}/${fatga_dir}
#mkdir -p "/home/${USER}/${fastga_dir}/fastga_results_plan1"
mkdir -p "/scratchbeta/${USER}/${fastga_dir}/fastga_results_planF"
mkdir -p "/scratchbeta/${USER}/${fastga_dir}/fastga_results_planA"
for data in ${ldir[@]} ; do
path_csv="${ldata}${data}"
plan_name=$(echo ${data} | sed "s/results_irace_plan//")
mexp=$(echo ${data[@]} | cut -d _ -f4)
mexp_id=$(echo ${mexp} | cut -d = -f2)
mevals=$(echo ${data[@]} | cut -d _ -f5)
mevals_id=$(echo ${mevals} | cut -d = -f2)
path="/scratchbeta/${USER}/${fastga_dir}/fastga_results_plan${plan_name[@]:0:1}"
cmd="bash ${file_py} ${path_csv} ${mexp_id} ${mevals_id} ${path}"
name="fastga${plan_name[@]:0:1}_${mexp}_${mevals}_$(date -Iseconds)_results_elites_all"
${cmd} &> "${path}/output${plan_name[@]:0:1}_fastga_${mexp}_${mevals}_$(date -Iseconds).txt"
done

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## This is an example of specifying instances with a file.
# Each line is an instance relative to trainInstancesDir
# (see scenario.txt.tmpl) and an optional sequence of instance-specific
# parameters that will be passed to target-runnerx when invoked on that
# instance.
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###################################################### -*- 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 = "./fastga.param"
## 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.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 = "."
## 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 = "./default.instances"
## File that contains a table of initial configurations. If empty or NULL,
## all initial configurations are randomly generated.
# configurationsFile = ""
## 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 = "./forbidden.txt"
## Script called for each configuration that executes the target algorithm
## to be tuned. See templates.
targetRunner = "./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 = 0 #100000
## Maximum total execution time in seconds for the executions of
## targetRunner. targetRunner must return two values: cost and time.
# maxTime = 60
## 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 = 2
## 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
## 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
## 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 = NA
## 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 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
############################################################################

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# name switch type range
# continuator "--continuator=" c (0)
crossoverrate "--crossover-rate=" r (0,1)
crossselector "--cross-selector=" c (0,1,2,3,4,5,6)
# aftercrossselector "--aftercross-selector=" c (0)
crossover "--crossover=" c (0,1,2,3,4,5,6,7,8,9)
mutationrate "--mutation-rate=" r (0,1)
mutselector "--mut-selector=" c (0,1,2,3,4,5,6)
mutation "--mutation=" c (0,1,2,3,4,5,6,7,8,9,10)
replacement "--replacement=" c (0,1,2,3,4,5,6,7,8,9,10)
popsize "--pop-size=" i (1,50)
offspringsize "--offspring-size=" i (1,50)

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#!/bin/bash
###############################################################################
# This script is the command that is executed every run.
# Check the examples in examples/
#
# This script is run in the execution directory (execDir, --exec-dir).
#
# PARAMETERS:
# $1 is the candidate configuration number
# $2 is the instance ID
# $3 is the seed
# $4 is the instance name
# The rest ($* after `shift 4') are parameters to the run
#
# RETURN VALUE:
# This script should print one numerical value: the cost that must be minimized.
# Exit with 0 if no error, with 1 in case of error
###############################################################################
error() {
echo "`TZ=UTC date`: $0: error: $@"
exit 1
}
EXE="./fastga"
LOG_DIR="irace_logs"
#FIXED_PARAMS="--problem=0"
#
CONFIG_ID=$1
INSTANCE_ID=$2
SEED=$3
INSTANCE=$(echo $4 | sed 's/\//\n/g'|tail -n 1)
CROSSOVER_RATE=$5
CROSSOVER_SELECTOR=$6
CROSSOVER=$7
MUTATION_RATE=$8
MUT_SELECTOR=$9
MUTATION=${10}
REPLACEMENT=${11}
POPSIZE=${12}
OFFSPRINGSIZE=${13}
shift 13 || error "Not enough parameters"
INSTANCE_PARAMS=$*
buckets=0
# STDOUT=${LOG_DIR}/c${CONFIG_ID}_i${INSTANCE_ID}_s${SEED}.stdout
# STDERR=${LOG_DIR}/c${CONFIG_ID}_i${INSTANCE_ID}_s${SEED}.stderr
STDOUT="/dev/null"
STDERR="/dev/null"
if [ ! -x "${EXE}" ]; then
error "${EXE}: not found or not executable (pwd: $(pwd))"
fi
# If the program just prints a number, we can use 'exec' to avoid
# creating another process, but there can be no other commands after exec.
#exec $EXE ${FIXED_PARAMS} -i $INSTANCE ${INSTANCE_PARAMS}
# exit 1
#
# Otherwise, save the output to a file, and parse the result from it.
# (If you wish to ignore segmentation faults you can use '{}' around
# the command.)
cmd="$EXE --problem=${INSTANCE} --instance=${INSTANCE} --seed=${SEED} ${CROSSOVER_RATE} ${CROSSOVER_SELECTOR} ${CROSSOVER} ${MUTATION_RATE} ${MUT_SELECTOR} ${MUTATION} ${REPLACEMENT} ${POPSIZE} ${OFFSPRINGSIZE} --max-evals=${buckets}"
# NOTE: irace seems to capture both stderr and stdout, so you should not output to stderr
echo ${cmd} > ${STDERR}
$cmd 2> ${STDERR} | tee ${STDOUT}
# The following code is useless if the binary only output a single number on stdout.
# This may be used to introduce a delay if there are filesystem
# issues.
# SLEEPTIME=1
# while [ ! -s "${STDOUT}" ]; do
# sleep $SLEEPTIME
# let "SLEEPTIME += 1"
# done
# This is an example of reading a number from the output.
# It assumes that the objective value is the first number in
# the first column of the last line of the output.
# if [ -s "${STDOUT}" ]; then
# COST=$(tail -n 1 ${STDOUT} | grep -e '^[[:space:]]*[+-]\?[0-9]' | cut -f1)
# echo "$COST"
# rm -f "${STDOUT}" "${STDERR}"
# exit 0
# else
# error "${STDOUT}: No such file or directory"
# fi

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## This is an example of specifying instances with a file.
# Each line is an instance relative to trainInstancesDir
# (see scenario.txt.tmpl) and an optional sequence of instance-specific
# parameters that will be passed to target-runnerx when invoked on that
# instance.
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###################################################### -*- 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 = "./fastga.param"
## 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.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 = "."
## 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 = "./default.instances"
## File that contains a table of initial configurations. If empty or NULL,
## all initial configurations are randomly generated.
# configurationsFile = ""
## 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 = "./forbidden.txt"
## Script called for each configuration that executes the target algorithm
## to be tuned. See templates.
targetRunner = "./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 = 0 #100000
## Maximum total execution time in seconds for the executions of
## targetRunner. targetRunner must return two values: cost and time.
# maxTime = 60
## 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 = 2
## 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
## 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
## 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 = NA
## 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 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
############################################################################

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# name switch type range
# continuator "--continuator=" c (0)
crossoverrate "--crossover-rate=" r (0,1)
crossselector "--cross-selector=" c (0,1,2,3,4,5,6)
# aftercrossselector "--aftercross-selector=" c (0)
crossover "--crossover=" c (0,1,2,3,4,5,6,7,8,9)
mutationrate "--mutation-rate=" r (0,1)
mutselector "--mut-selector=" c (0,1,2,3,4,5,6)
mutation "--mutation=" c (0,1,2,3,4,5,6,7,8,9,10)
replacement "--replacement=" c (0,1,2,3,4,5,6,7,8,9,10)
popsize "--pop-size=" i (1,50)
offspringsize "--offspring-size=" i (1,50)

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#!/bin/bash
###############################################################################
# This script is the command that is executed every run.
# Check the examples in examples/
#
# This script is run in the execution directory (execDir, --exec-dir).
#
# PARAMETERS:
# $1 is the candidate configuration number
# $2 is the instance ID
# $3 is the seed
# $4 is the instance name
# The rest ($* after `shift 4') are parameters to the run
#
# RETURN VALUE:
# This script should print one numerical value: the cost that must be minimized.
# Exit with 0 if no error, with 1 in case of error
###############################################################################
error() {
echo "`TZ=UTC date`: $0: error: $@"
exit 1
}
EXE="./fastga"
LOG_DIR="irace_logs"
FIXED_PARAMS="--problem=0"
#MAX_EVALS=2
#
CONFIG_ID=$1
INSTANCE_ID=$2
SEED=$3
INSTANCE=$(echo $4 | sed 's/\//\n/g'|tail -n 1)
CROSSOVER_RATE=$5
CROSSOVER_SELECTOR=$6
CROSSOVER=$7
MUTATION_RATE=$8
MUT_SELECTOR=$9
MUTATION=${10}
REPLACEMENT=${11}
POPSIZE=${12}
OFFSPRINGSIZE=${13}
shift 13 || error "Not enough parameters"
INSTANCE_PARAMS=$*
buckets=0
#dim=(20 20 16 48 25 32 128 128 128 50 100 150 128 192 192 192 256 75 150)
#size= $( echo $(echo $13 | cut -d'=' -f2))
#pb=$(echo $(echo $13 | cut -d'=' -f2))
#maxevals=$( echo ${dim[$pb]})
# STDOUT=${LOG_DIR}/c${CONFIG_ID}_i${INSTANCE_ID}_s${SEED}.stdout
# STDERR=${LOG_DIR}/c${CONFIG_ID}_i${INSTANCE_ID}_s${SEED}.stderr
STDOUT="/dev/null"
STDERR="/dev/null"
if [ ! -x "${EXE}" ]; then
error "${EXE}: not found or not executable (pwd: $(pwd))"
fi
# If the program just prints a number, we can use 'exec' to avoid
# creating another process, but there can be no other commands after exec.
#exec $EXE ${FIXED_PARAMS} -i $INSTANCE ${INSTANCE_PARAMS}
# exit 1
#
# Otherwise, save the output to a file, and parse the result from it.
# (If you wish to ignore segmentation faults you can use '{}' around
# the command.)
cmd="$EXE ${FIXED_PARAMS} --instance=${INSTANCE} --seed=${SEED} ${CROSSOVER_RATE} ${CROSSOVER_SELECTOR} ${CROSSOVER} ${MUTATION_RATE} ${MUT_SELECTOR} ${MUTATION} ${REPLACEMENT} ${POPSIZE} ${OFFSPRINGSIZE} --max-evals=${buckets}"
# NOTE: irace seems to capture both stderr and stdout, so you should not output to stderr
echo ${cmd} > ${STDERR}
$cmd 2> ${STDERR} | tee ${STDOUT}
# The following code is useless if the binary only output a single number on stdout.
# This may be used to introduce a delay if there are filesystem
# issues.
# SLEEPTIME=1
# while [ ! -s "${STDOUT}" ]; do
# sleep $SLEEPTIME
# let "SLEEPTIME += 1"
# done
# This is an example of reading a number from the output.
# It assumes that the objective value is the first number in
# the first column of the last line of the output.
# if [ -s "${STDOUT}" ]; then
# COST=$(tail -n 1 ${STDOUT} | grep -e '^[[:space:]]*[+-]\?[0-9]' | cut -f1)
# echo "$COST"
# rm -f "${STDOUT}" "${STDERR}"
# exit 0
# else
# error "${STDOUT}: No such file or directory"
# fi

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#!/usr/bin/env python3
#parse data1
import os
import re
import sys
print("ecdf,id,crossover-rate,cross-selector,crossover,mutation-rate,mut-selector,mutation,replacement,pop-size,offspring-size")
#give the path of one experiment
argv=sys.argv[1]
for datadir in os.listdir(argv):
#if(os.path.isdir(os.path.join(argv,datadir))): check if argv/datadir is a directory
if(datadir.find("results_irace")>=0): #check if the directory is one JOB
with open(os.path.join("./",argv,datadir,"irace.log")) as fd:
data = fd.readlines()
# Find the last best configuration
bests = [line.strip() for line in data if "Best-so-far" in line]
#print(datadir,bests)
best = bests[-1].split()
best_id, best_perf = best[2], best[5]
# print(best_id,best_perf)
# Filter the config detail
configs = [line.strip() for line in data if "--crossover-rate=" in line and best_id in line]
# print(configs)
# Format as CSV
algo = re.sub("\-\-\S*=", ",", configs[0])
csv_line = best_perf + "," + algo
print(csv_line.replace(" ",""))

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#!/usr/bin/env python3
#parse data1
import os
import re
import sys
#print("pb,ecdf,id,crossover-rate,cross-selector,crossover,mutation-rate,mut-selector,mutation,replacement") #plan1
print("pb,ecdf,id,crossover-rate,cross-selector,crossover,mutation-rate,mut-selector,mutation,replacement,pop-size,offspring-size")
#give the path of one experiment
argv=sys.argv[1]
for datadir in os.listdir(argv):
#if(os.path.isdir(os.path.join(argv,datadir))): check if argv/datadir is a directory
if(datadir.find("results_irace")>=0): #check if the directory is one JOB
for pb_dir in os.listdir(os.path.join(argv,datadir)):
if "results_problem" in pb_dir:
pb_id=pb_dir.replace("results_problem_","")
with open(os.path.join("./",argv,datadir,pb_dir,"irace.log")) as fd:
data = fd.readlines()
# Find the last best configuration
bests = [line.strip() for line in data if "Best-so-far" in line]
#print(datadir,bests)
best = bests[-1].split()
best_id, best_perf = best[2], best[5]
# print(best_id,best_perf)
# Filter the config detail
configs = [line.strip() for line in data if "--crossover-rate=" in line and best_id in line]
# print(configs)
# Format as CSV
algo = re.sub("\-\-\S*=", ",", configs[0])
csv_line = pb_id + "," + best_perf + "," + algo
print(csv_line.replace(" ",""))

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#!/bin/bash
#run once each problem
echo "-------------------------Start the JOB : $(date --iso-8601=seconds)"
. /etc/profile.d/modules.sh
export MODULEPATH=${MODULEPATH}${MODULEPATH:+:}/opt/dev/Modules/Anaconda:/opt/dev/Modules/Compilers:/opt/dev/Modules/Frameworks:/opt/dev/Modules/Libraries:/opt/dev/Modules/Tools:/opt/dev/Modules/IDEs:/opt/dev/Modules/MPI
module load LLVM/clang-llvm-10.0
module load R
dir=$1
run=$2
budget_irace=$3
buckets=$4
myhome=$5
cp -r ${myhome}/R .
cp -r ${myhome}/irace_files_pA .
#cp -r /scratchbeta/zhenga/irace_files .
#chmod u+x ./fastga
outdir="${run}_$(date --iso-8601=seconds)_results_irace"
rundir=${dir}/${outdir}
mkdir -p ${rundir}
# Fore some reason, irace absolutely need those files...
cp ${myhome}/code/paradiseo/eo/contrib/irace/release/fastga ${rundir}
cat ./irace_files_pA/example.scen | sed "s%\".%\"${rundir}%g" | sed "s/maxExperiments = 0/maxExperiments=${budget_irace}/" > ${rundir}/example.scen
cp ./irace_files_pA/default.instances ${rundir}
cp ./irace_files_pA/fastga.param ${rundir}
cp ./irace_files_pA/forbidden.txt ${rundir}
cat ./irace_files_pA/target-runner | sed "s/buckets=0/buckets=${buckets}/" > ${rundir}/target-runner
chmod u+x ${rundir}/target-runner
echo "---start $(date)"
time -p ./R/x86_64-pc-linux-gnu-library/3.6/irace/bin/irace --scenario ${rundir}/example.scen > ${rundir}/irace.log
echo "---end $(date)"
echo "End the JOB : $(date --iso-8601=seconds)------------------------------"

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#!/bin/bashi
myhome=$1
scratchpath=$2
mexp=$3
mevals=$4
date -Iseconds
echo "STARTS"
dir=${scratchpath}/dataFAR/dataA
#dir=${HOME}/plan4/${name}
#cat ${HOME}/irace_files_pA/example.scen |sed "s/maxExperiments = 0/maxExperiments = ${mexp}/" > ${HOME}/irace_files_pA/example.scen
mkdir -p ${dir}
outdir="${dir}/dataA_maxExp=${mexp}_maxEv=${mevals}_$(date --iso-8601=seconds)"
mkdir -p ${outdir}
for r in $(seq 2); do
echo "Run $r/15";
cmd="qsub -N iraceA_maxEv_${r} -q beta -l select=1:ncpus=1 -l walltime=00:30:00 -- ${scratchpath}/planA/r_iA.sh ${outdir} ${r} ${mexp} ${mevals} ${myhome}"
#cmd="bash ./r_iA_buckets.sh ${outdir} ${r} ${mexp} ${mevals}"
echo $cmd
time -p $cmd
done
echo "DONE"
#cat ${HOME}/irace_files_pA/example.scen |sed "s/maxExperiments = ${mexp}/maxExperiments = 0/" > ${HOME}/irace_files_pA/example.scen
date -Iseconds

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#!/bin/bash
#run once each problem
dir=$1
run=$2
budget_irace=$3
buckets=$4
myhome=$5
echo "---------------start JOB ${run} $(date --iso-8601=seconds)"
. /etc/profile.d/modules.sh
export MODULEPATH=${MODULEPATH}${MODULEPATH:+:}/opt/dev/Modules/Anaconda:/opt/dev/Modules/Compilers:/opt/dev/Modules/Frameworks:/opt/dev/Modules/Libraries:/opt/dev/Modules/Tools:/opt/dev/Modules/IDEs:/opt/dev/Modules/MPI
module load LLVM/clang-llvm-10.0
module load R
cp -r ${myhome}/R .
cp -r ${myhome}/irace_files_pF .
#cp -r /scratchbeta/zhenga/irace_files .
#chmod u+x ./fastga
outdir="${run}_$(date --iso-8601=seconds)_results_irace"
for pb in $(seq 0 18) ; do
echo "Problem ${pb}... "
res="results_problem_${pb}"
mkdir -p ${dir}/${outdir}/${res}
# Fore some reason, irace absolutely need those files...
cp ${myhome}/code/paradiseo/eo/contrib/irace/release/fastga ${dir}/${outdir}/${res}
cat ./irace_files_pF/example.scen | sed "s%\".%\"${dir}/${outdir}/${res}%g" | sed "s/maxExperiments = 0/maxExperiments=${budget_irace}/" > ${dir}/${outdir}/${res}/example.scen
cp ./irace_files_pF/default.instances ${dir}/${outdir}/${res}
cp ./irace_files_pF/fastga.param ${dir}/${outdir}/${res}
cp ./irace_files_pF/forbidden.txt ${dir}/${outdir}/${res}
cat ./irace_files_pF/target-runner | sed "s/--problem=0/--problem=${p}/" | sed "s/buckets=0/buckets=${buckets}/" > ${dir}/${outdir}/${res}/target-runner
chmod u+x ${dir}/${outdir}/${res}/target-runner
echo "---start $(date)"
time -p ./R/x86_64-pc-linux-gnu-library/3.6/irace/bin/irace --scenario ${dir}/${outdir}/${res}/example.scen > ${dir}/${outdir}/${res}/irace.log
echo "---end $(date)"
done
echo "end JOB ${run} $(date --iso-8601=seconds)---------------"

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#!/bin/bash
date -Iseconds
echo "STARTS"
myhome=$1
scratchpath=$2
#dir=${HOME}/plan2/${name}
mexp=$3 #budget irace
mevals=$4 #budget fastga
name="dataF_maxExp=${mexp}_maxEv=${mevals}_$(date --iso-8601=seconds)"
dir=${scratchpath}/dataFAR/dataF/${name}
mkdir -p ${dir}
for r in $(seq 2); do
echo "Run $r/15";
#date -Iseconds
#cmd="qsub -N irace_${runs}_${buckets}" -q beta -l select=1:ncpus=1 -l walltime=00:04:00 --${HOME}/run_irace.sh ${dir}
cmd="qsub -N iraceF_${mevals}_run=${r} -q beta -l select=1:ncpus=1 -l walltime=00:30:00 -- ${scratchpath}/planF/r_iF.sh ${dir} ${r} ${mexp} ${mevals} ${myhome}"
#time -p bash ${HOME}/plan2/run_irace2.sh ${dir} ${r} &> ${dir}/erreur_${r}.txt
#bash ${HOME}/test/r_i.sh
echo $cmd
$cmd
#date -Iseconds
done
#echo "DONE"
#date -Iseconds
#echo $(pwd)

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#!/bin/bash
#instance = seed
echo "-----------------Start $(date)"
. /etc/profile.d/modules.sh
export MODULEPATH=${MODULEPATH}${MODULEPATH:+:}/opt/dev/Modules/Anaconda:/opt/dev/Modules/Compilers:/opt/dev/Modules/Frameworks:/opt/dev/Modules/Libraries:/opt/dev/Modules/Tools:/opt/dev/Modules/IDEs:/opt/dev/Modules/MPI
module load LLVM/clang-llvm-10.0
csv_file=$1 #contains all the configs of all the problems of one experiments
mexp=$2
mevals=$3
path=$4
# Number of runs (=seeds).
runs=50
# You most probably want to run on release builds.
exe="/home/zhenga/fastga"
plan=$(echo ${csv_file} | sed "s/results_irace_plan//")
outdir="${path}/plan4_maxExp=${mexp}_maxEv=${mevals}_$(date --iso-8601=minutes)_results_elites_all"
mkdir -p ${outdir}
mkdir -p ${outdir}/raw
mkdir -p ${outdir}/raw/data
mkdir -p ${outdir}/raw/logs
n=0
algoid=0
for line in $(cat ${csv_file}| sed 1,1d | cut -s -d"," -f3-11 ); do
a=($(echo $line | sed "s/,/ /g"))
algo="--crossover-rate=${a[0]} --cross-selector=${a[1]} --crossover=${a[2]} --mutation-rate=${a[3]} --mut-selector=${a[4]} --mutation=${a[5]} --replacement=${a[6]} --pop-size=${a[7]} --offspring-size=${a[8]}"
#perc=$(echo "scale=3;${n}/(285)*100.0" | bc)
#echo "${perc}% : algo ${algoid}/285"
# echo -n "Runs: "
for pb in $(seq 0 18) ; do
name_dir="pb=${pb}_$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
mkdir -p ${outdir}/raw/data/${name_dir}
mkdir -p ${outdir}/raw/logs/${name_dir}
for seed in $(seq ${runs}) ; do # Iterates over runs/seeds.
# This is the command to be ran.
#cmd="${exe} --full-log=1 --problem=${pb} --seed=${seed} ${algo}"
cmd="${exe} --problem=${pb} --seed=${seed} --instance=${seed} ${algo}"
#echo ${cmd} # Print the command.
# Forge a directory/log file name
# (remove double dashs and replace spaces with underscore).
name_run="pb=${pb}_seed=${seed}_$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
# echo $name_run
# Actually start the command.
${cmd} > "${outdir}/raw/data/${name_dir}/${name_run}.dat" 2> "${outdir}/raw/logs/${name_dir}/${name_run}.log"
# Check for the most common problem in the log file.
#cat "${outdir}/raw/logs/${name_run}.log" | grep "illogical performance"
done # seed
n=$(($n+1))
done
algoid=$(($algoid+1))
done
# Move IOH logs in the results directory.
#mv ./FastGA_* ${outdir}
echo "Done $(date) -----------------------"
#date

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#!/bin/bash
#instance = seed
echo "-----------------Start $(date)"
. /etc/profile.d/modules.sh
export MODULEPATH=${MODULEPATH}${MODULEPATH:+:}/opt/dev/Modules/Anaconda:/opt/dev/Modules/Compilers:/opt/dev/Modules/Frameworks:/opt/dev/Modules/Libraries:/opt/dev/Modules/Tools:/opt/dev/Modules/IDEs:/opt/dev/Modules/MPI
module load LLVM/clang-llvm-10.0
csv_file=$1 #contains all the configs of all the problems of one experiments
mexp=$2
mevals=$3
path=$4
# Number of runs (=seeds).
runs=50
# You most probably want to run on release builds.
exe="/home/${USER}/fastga"
plan=$(echo ${csv_file} | cut -d / -f3 | sed "s/results_irace_plan//")
outdir="${path}/plan${plan[@]:0:1}_maxExp=${mexp}_maxEv=${mevals}_$(date --iso-8601=minutes)_results_elites_all"
mkdir -p ${outdir}
mkdir -p ${outdir}/raw
mkdir -p ${outdir}/raw/data
mkdir -p ${outdir}/raw/logs
n=0
algoid=0
for line in $(cat ${csv_file}| sed 1,1d ); do
a=($(echo $line | sed "s/,/ /g"))
algo="--crossover-rate=${a[3]} --cross-selector=${a[4]} --crossover=${a[5]} --mutation-rate=${a[6]} --mut-selector=${a[7]} --mutation=${a[8]} --replacement=${a[9]} --pop-size=${a[10]} --offspring-size=${a[11]}"
#perc=$(echo "scale=3;${n}/(285)*100.0" | bc)
#echo "${perc}% : algo ${algoid}/285"
# echo -n "Runs: "
name_dir="pb=${a[0]}_$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
mkdir -p ${outdir}/raw/logs/${name_dir}
mkdir -p ${outdir}/raw/data/${name_dir}
for seed in $(seq ${runs}) ; do # Iterates over runs/seeds.
# This is the command to be ran.
#cmd="${exe} --full-log=1 --problem=${pb} --seed=${seed} ${algo}"
cmd="${exe} --problem=${a[0]} --seed=${seed} --instance=${seed} ${algo}"
#echo ${cmd} # Print the command.
# Forge a directory/log file name
# (remove double dashs and replace spaces with underscore).
name_run="pb=${a[0]}_seed=${seed}_$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
# echo $name_run
# Actually start the command.
${cmd} > "${outdir}/raw/data/${name_dir}/${name_run}.dat" 2> "${outdir}/raw/logs/${name_dir}/${name_run}.log"
# Check for the most common problem in the log file.
#cat "${outdir}/raw/logs/${name_run}.log" | grep "illogical performance"
done # seed
n=$(($n+1))
algoid=$(($algoid+1))
done
# Move IOH logs in the results directory.
#mv ./FastGA_* ${outdir}
echo "Done $(date) -----------------------"
#date

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#!/bin/bash
lexp=(300 600 1000 10000)
levals=(100 500 1000)
myscratchpath=/scratchbeta/$USER
myhome=${HOME}
for exp in ${lexp[@]} ; do
for evals in ${levals[@]} ; do
bash ./planF/riaF.sh ${myhome} ${myscratchpath} ${exp} ${evals}
bash ./planA/riaA.sh ${myhome} ${scratchpath} ${exp} ${evals}
done
done
bash testrandom.sh ${myhome} ${scratchpath} ${levals[@]}

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#!/bin/bash
# Number of runs (=seeds).
runs=5
basename=$1
mevals=$2
nbAlgo=2
echo "Start JOB maxEv= $mevals $(date -Iseconds) ----------------------"
. /etc/profile.d/modules.sh
export MODULEPATH=${MODULEPATH}${MODULEPATH:+:}/opt/dev/Modules/Anaconda:/opt/dev/Modules/Compilers:/opt/dev/Modules/Frameworks:/opt/dev/Modules/Libraries:/opt/dev/Modules/Tools:/opt/dev/Modules/IDEs:/opt/dev/Modules/MPI
module load LLVM/clang-llvm-10.0
cp ${HOME}/code/paradiseo/eo/contrib/irace/release/fastga .
# You most probably want to run on release builds.
exe="./fastga"
#outdir="/scratchbeta/$USER/$(date --iso-8601=minutes)_results_randoms"
outdir="${basename}/maxEv=${mevals}_nbAlgo=${nbAlgo}_$(date --iso-8601=minutes)_results_randoms"
mkdir -p ${outdir}
n=1
algoid=0
for algoid in $(seq ${nbAlgo}); do
#date
r1=$(echo "scale=2 ; ${RANDOM}/32767" | bc)
r2=$(echo "scale=2 ; ${RANDOM}/32767" | bc)
a=(${r1} $((RANDOM%7)) $((RANDOM%10)) ${r2} $((RANDOM%7)) $((RANDOM%11)) $((RANDOM%11)) $((RANDOM%50 +1)) $((RANDOM%50 +1)) )
#condition for value of replacement, pop-size and offspringsize
while [[ (1 -lt ${a[6]} && ${a[7]} -lt ${a[8]}) || ( ${a[6]} -eq 1 && ${a[7]} -ne ${a[8]}) ]]
do
#echo "get in ------------------replacement ${a[6]} popsize ${a[7]} offspringsize ${a[8]}"
r1=$(echo "scale=2 ; ${RANDOM}/32767" | bc)
r2=$(echo "scale=2 ; ${RANDOM}/32767" | bc)
a=(${r1} $((RANDOM%7)) $((RANDOM%10)) ${r2} $((RANDOM%7)) $((RANDOM%11)) $((RANDOM%11)) $((RANDOM%50 +1)) $((RANDOM%50 +1)))
done
algo="--crossover-rate=${a[0]} --cross-selector=${a[1]} --crossover=${a[2]} --mutation-rate=${a[3]} --mut-selector=${a[4]} --mutation=${a[5]} --replacement=${a[6]} --pop-size=${a[7]} --offspring-size=${a[8]}"
echo " start algo ${a}------ $(date --iso-8601=minutes)"
algodir="$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
for pb in $(seq 0 18) ; do
perc=$(echo "scale=3;${n}/(10*18)*10.0" | bc)
#echo "${perc}% : algo ${algoid}/100, problem ${pb}/18 $(date --iso-8601=minutes)"
# echo -n "Runs: "
name_dir="pb=${pb}_$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
mkdir -p ${outdir}/${algodir}/data/${name_dir}
mkdir -p ${outdir}/${algodir}/logs/${name_dir}
for seed in $(seq ${runs}) ; do # Iterates over runs/seeds.
# This is the command to be ran.
cmd="${exe} --problem=${pb} --seed=${seed} --instance=${seed} ${algo} --max-evals=${mevals}"
name_run="pb=${pb}_seed=${seed}_$(echo "${algo}" | sed 's/--//g' | sed 's/ /_/g')"
# echo $name_run
#echo $algo
${cmd} > ${outdir}/${algodir}/data/${name_dir}/${name_run}.dat 2> ${outdir}/${algodir}/logs/${name_dir}/${name_run}.log
# Check for the most common problem in the log file.
#cat "${outdir}/raw/logs/${name_run}.log" | grep "illogical performance"
done # seed
# echo ""
n=$(($n+1))
done # pb
echo "end algo $(date -Iseconds) "
algoid=$(($algoid+1))
done
echo "------------------------------------Done $mevals $(date -Iseconds) "

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#!/bin/bash
#get csv file, parse dataF in a csv file
dir=/scratchbeta/$USER/dataFAR
listdir=$(echo $(ls ${dir}))
for data in ${listdir[@]} ; do
file_py="parse${data: -1}_irace_bests.py"
path="${dir}/${data}"
cmd="bash ./csv_all_bests.sh ${path} ${file_py}"
echo $cmd
$cmd
done
#get validation run of each config
dir=/scratchbeta/$USER/csv_FA
listdir=$(echo $(ls ${dir}))
echo ${listdir[@]}
for csvdir in ${listdir[@]} ; do
csvpath="${dir}/${csvdir}"
file_py="./run_elites_plan${csvdir: -1}.sh"
cmd="bash ./fastga_elites_all.sh ${csvpath} ${file_py}"
echo $cmd
$cmd
done

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#!/bin/bash
#tab=(15000 20000 30000 40000)
#tab=(100 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 10000)
myhome=$1
scratchpath=$2
tab=${@:3}
#echo ${tab[@]}
outdir="/scratchbeta/$USER/fastga_results_all/fastga_results_random"
mkdir -p ${outdir} #results of random experiment
for evals in ${tab[@]}; do
#evalsdir="${name}/maxEv=${evals}"
#mkdir -p ${evalsdir}
#{ time -p bash /home/$USER/run_random.sh ${name} ${i} 50 ; } &> "${name}/sortie5_${j}_maxExp=${i}.txt"
#cmd="qsub -N iraceR_maxEv=${evals} -q beta -l select=1:ncpus=1 -l walltime=00:30:00 -- /scratchbeta/$USER/run_random.sh ${outdir} ${evals}"
$cmd
done