Repository of scripts used by IRIDIA members

Max's scripts

When testing different algorithms on some combinatorial optimization problem, I usually organize the data on my machine according to a same schema. Basically I create the following structure of directories to store sources, problem instances, output data, executables and scripts.

+ main_project_folder
|
|-+ bin   (contains the executables of the algorithms)
|
|-+ instances   (contains the problem instances)
|
|-+ out   (contains the outputs of the trial)
| |
| |-+ algorithm1
| | |
| | |-+ instance1
| | | 
| | |-+ instance2
| |
| |-+ algorithm2
|   |
|   |-+ instance1
|   | 
|   |-+ instance2
|
|-+ sh   (contains the script to launch the experiments)
|
|-+ src   (contains the sources of the algorithms)
| |
| |-+ algorithm1
| | 
| |-+ algorithm2
|
|-+ analysis    (contains the R script to boxplot the experimental data)

Boxplot of solution values by algorithm

I organize the data in the following way:

in the file optimal_values.txt I put the best-known solution values for the problem instances I'm testing

instance   optimum 
sko100a    152002 
sko100e    149150 
tai100a    21059006 
tai100b    1185996137 

in the files algorithms.txt I put the algorithms details

idalgo          topo    schema  ls      type    number_of_cpus
PIR-2opt        PIR     PIR     2       Par     2
PIR-ts          PIR     PIR     3       Par     2
SEQ-2opt        SEQ     SEQ     2       Par     2
SEQ-ts          SEQ     SEQ     3       Par     2
SEQ2-2opt       SEQ     SEQ     2       Par     2
SEQ2-ts         SEQ     SEQ     3       Par     2
HC.1.x.10-2opt  HC      1.x.10  2       Par     2
HC.2.6.10-2opt  HC      2.6.10  2       Par     2
HC.2.7.10-2opt  HC      2.7.10  2       Par     2

in the files algorithm_factors_instance-size_cut-time.txt I record the history of the search of the algo for the instance

idalgo          cpu_id  instance        try     best    time    iteration
HC.1.x.10-2opt  0       sko100a         1       155254  0.35    1
HC.1.x.10-2opt  0       sko100a         1       154162  0.35    1
HC.1.x.10-2opt  0       sko100a         1       154050  0.35    1
HC.1.x.10-2opt  0       sko100a         1       153684  2.69    8
HC.1.x.10-2opt  0       sko100a         1       153508  3.24    10
HC.1.x.10-2opt  0       sko100a         1       153396  3.24    10
HC.1.x.10-2opt  0       sko100a         1       153344  3.24    10
HC.1.x.10-2opt  0       sko100a         1       153092  3.52    11
HC.1.x.10-2opt  0       sko100a         1       153062  3.75    12

In order to produce boxplots like the one above you can look at the R source used to produce it.

optimal_values<-read.table("optimal_values.txt",header=TRUE)

resPIR2OPT<-read.table("parallel_independent_2-opt_100_1000.txt",header=TRUE)
resSEQ2OPT<-read.table("sequential_2-opt_100_2000.txt",header=TRUE)
resSEQ22OPT<-read.table("sequential2_2-opt_100_1000.txt",header=TRUE)
resHC1x102OPT<-read.table("hc.1.x.10_2-opt_100_1000.txt",header=TRUE)
resHC26102OPT<-read.table("hc.2.6.10_2-opt_100_1000.txt",header=TRUE)
resHC27102OPT<-read.table("hc.2.7.10_2-opt_100_1000.txt",header=TRUE)
resHC28102OPT<-read.table("hc.2.8.10_2-opt_100_1000.txt",header=TRUE)
resHC29102OPT<-read.table("hc.2.9.10_2-opt_100_1000.txt",header=TRUE)
resHC36102OPT<-read.table("hc.3.6.10_2-opt_100_1000.txt",header=TRUE)
resHC37102OPT<-read.table("hc.3.7.10_2-opt_100_1000.txt",header=TRUE)
resHC38102OPT<-read.table("hc.3.8.10_2-opt_100_1000.txt",header=TRUE)
resHC39102OPT<-read.table("hc.3.9.10_2-opt_100_1000.txt",header=TRUE)
resRW1x102OPT<-read.table("rw.1.x.10_2-opt_100_1000.txt",header=TRUE)
resRW26102OPT<-read.table("rw.2.6.10_2-opt_100_1000.txt",header=TRUE)
resRW27102OPT<-read.table("rw.2.7.10_2-opt_100_1000.txt",header=TRUE)
resRW28102OPT<-read.table("rw.2.8.10_2-opt_100_1000.txt",header=TRUE)
resRW29102OPT<-read.table("rw.2.9.10_2-opt_100_1000.txt",header=TRUE)
resRW36102OPT<-read.table("rw.3.6.10_2-opt_100_1000.txt",header=TRUE)
resRW37102OPT<-read.table("rw.3.7.10_2-opt_100_1000.txt",header=TRUE)
resRW38102OPT<-read.table("rw.3.8.10_2-opt_100_1000.txt",header=TRUE)
resRW39102OPT<-read.table("rw.3.9.10_2-opt_100_1000.txt",header=TRUE)

res<-rbind(resRW1x102OPT,resRW26102OPT,resRW27102OPT,resRW28102OPT,resRW29102OPT,
resRW36102OPT,resRW37102OPT,resRW38102OPT,resRW39102OPT,resHC1x102OPT,resHC26102OPT,
resHC27102OPT,resHC28102OPT,resHC29102OPT,resHC36102OPT,resHC37102OPT,resHC38102OPT,
resHC39102OPT,resPIR2OPT,resSEQ2OPT,resSEQ22OPT)


linstance<-levels(res$instance)

res.split<-split(1:nrow(res), list(res$instance, res$try, res$idalgo), drop=TRUE)

min.list <- lapply(res.split, function(x){
        x[match(min(res$best[x]), res$best[x])]
        })

# matches return the first among all the values with min best!!!
# so is not the one with minimal time

min.vector <- unlist(min.list)

bestalgo<-res[min.vector,]

bestalgo.split <- split(1:nrow(bestalgo), bestalgo$instance, drop=TRUE)

for (i in (1:length(bestalgo.split)))
{
        bestalgo.vector <- unlist(bestalgo.split[i])
        bestalgo.temp <- bestalgo[bestalgo.vector,]
        l<-split(as.double(bestalgo.temp$best),bestalgo.temp$idalgo)

        epsfile=paste(linstance[i],"_1000.eps",sep="")
        postscript(file=epsfile,onefile=TRUE,horizontal=TRUE)
        par(mar=c(5,5,5,3),cex.axis=0.7,las=2,mgp=c(4, 1, 0))
        title_plot=paste("2 CPU - 1000 iterations - instance ",linstance[i],sep="")
        boxplot(l,xlab="",ylab="solution value",names=c(levels(bestalgo$idalgo)),main=title_plot,
yaxt="n",ylim=c(min(min(bestalgo.temp$best),optimal_values[optimal_values$instance==linstance[i],]$optimum),
max(bestalgo.temp$best)))
        axis(2, seq(from=min(min(bestalgo.temp$best),optimal_values[optimal_values$instance==linstance[i],]$optimum),
to=max(bestalgo.temp$best),length.out=10))
        abline(h=optimal_values[optimal_values$instance==linstance[i],]$optimum)
        grid(nx=0, ny=55,col="gray5")
        dev.off()
}