Repository of R scripts used by IRIDIA members
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Revision as of 21:26, 11 August 2006 by Mmanfrin (talk | contribs) (→Boxplot of solution values by algorithm)
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Boxplot of solution values by algorithm
I organize the data in the following way:
in the file optimal_values_xxx.txt i put the best-known solution values for the problem instances I'm testing
cat optimal_values_100.txt instance optimum group sko100a 152002 1 sko100e 149150 1 tai100a 21059006 1 tai100b 1185996137 1
in the files algo.factori.instance_size.cut_time.txt i record the history of the search of the algo for the instance
euclides:~/Desktop/Parallel-QAP/out/analysis/2-opt mmanfrin$ head sequential_2-opt_100_8000.txt idalgo topo schema ls type cpu_id instance try best time iteration SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 155468 0.31 1 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 155390 0.31 1 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 155000 0.31 1 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 154934 0.64 2 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 154608 0.97 3 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153958 1.27 4 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153750 1.93 6 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153720 1.93 6 SEQ-2opt SEQ SEQ 2 Seq 0 sko100a 1 153634 2.57 8
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_100.txt",header=TRUE) resPIR2OPT<-read.table("parallel_independent_2-opt_100_100.txt",header=TRUE) resSEQ2OPT<-read.table("sequential_2-opt_100_800.txt",header=TRUE) resSEQ22OPT<-read.table("sequential2_2-opt_100_100.txt",header=TRUE) resFC1x102OPT<-read.table("fc.1.x.10_2-opt_100_100.txt",header=TRUE) resFC26102OPT<-read.table("fc.2.6.10_2-opt_100_100.txt",header=TRUE) resFC27102OPT<-read.table("fc.2.7.10_2-opt_100_100.txt",header=TRUE) resFC28102OPT<-read.table("fc.2.8.10_2-opt_100_100.txt",header=TRUE) resFC29102OPT<-read.table("fc.2.9.10_2-opt_100_100.txt",header=TRUE) resFC36102OPT<-read.table("fc.3.6.10_2-opt_100_100.txt",header=TRUE) resFC37102OPT<-read.table("fc.3.7.10_2-opt_100_100.txt",header=TRUE) resFC38102OPT<-read.table("fc.3.8.10_2-opt_100_100.txt",header=TRUE) resFC39102OPT<-read.table("fc.3.9.10_2-opt_100_100.txt",header=TRUE) resHC1x102OPT<-read.table("hc.1.x.10_2-opt_100_100.txt",header=TRUE) resHC26102OPT<-read.table("hc.2.6.10_2-opt_100_100.txt",header=TRUE) resHC27102OPT<-read.table("hc.2.7.10_2-opt_100_100.txt",header=TRUE) resHC28102OPT<-read.table("hc.2.8.10_2-opt_100_100.txt",header=TRUE) resHC29102OPT<-read.table("hc.2.9.10_2-opt_100_100.txt",header=TRUE) resHC36102OPT<-read.table("hc.3.6.10_2-opt_100_100.txt",header=TRUE) resHC37102OPT<-read.table("hc.3.7.10_2-opt_100_100.txt",header=TRUE) resHC38102OPT<-read.table("hc.3.8.10_2-opt_100_100.txt",header=TRUE) resHC39102OPT<-read.table("hc.3.9.10_2-opt_100_100.txt",header=TRUE) resRW1x102OPT<-read.table("rw.1.x.10_2-opt_100_100.txt",header=TRUE) resRW26102OPT<-read.table("rw.2.6.10_2-opt_100_100.txt",header=TRUE) resRW27102OPT<-read.table("rw.2.7.10_2-opt_100_100.txt",header=TRUE) resRW28102OPT<-read.table("rw.2.8.10_2-opt_100_100.txt",header=TRUE) resRW29102OPT<-read.table("rw.2.9.10_2-opt_100_100.txt",header=TRUE) resRW36102OPT<-read.table("rw.3.6.10_2-opt_100_100.txt",header=TRUE) resRW37102OPT<-read.table("rw.3.7.10_2-opt_100_100.txt",header=TRUE) resRW38102OPT<-read.table("rw.3.8.10_2-opt_100_100.txt",header=TRUE) resRW39102OPT<-read.table("rw.3.9.10_2-opt_100_100.txt",header=TRUE) resUR1x102OPT<-read.table("ur.1.x.10_2-opt_100_100.txt",header=TRUE) resUR26102OPT<-read.table("ur.2.6.10_2-opt_100_100.txt",header=TRUE) resUR27102OPT<-read.table("ur.2.7.10_2-opt_100_100.txt",header=TRUE) resUR28102OPT<-read.table("ur.2.8.10_2-opt_100_100.txt",header=TRUE) resUR29102OPT<-read.table("ur.2.9.10_2-opt_100_100.txt",header=TRUE) resUR36102OPT<-read.table("ur.3.6.10_2-opt_100_100.txt",header=TRUE) resUR37102OPT<-read.table("ur.3.7.10_2-opt_100_100.txt",header=TRUE) resUR38102OPT<-read.table("ur.3.8.10_2-opt_100_100.txt",header=TRUE) resUR39102OPT<-read.table("ur.3.9.10_2-opt_100_100.txt",header=TRUE) res<-rbind(resFC1x102OPT,resFC26102OPT,resFC27102OPT,resFC28102OPT,resFC29102OPT,resFC36102OPT,resFC37102OPT,resFC38102OPT,resFC39102OPT,resRW1x102OPT,resRW26102OPT,resRW27102OPT,resRW28102OPT,resRW29102OPT,resRW36102OPT,resRW37102OPT,resRW38102OPT,resRW39102OPT,resHC1x102OPT,resHC26102OPT,resHC27102OPT,resHC28102OPT,resHC29102OPT,resHC36102OPT,resHC37102OPT,resHC38102OPT,resHC39102OPT,resUR1x102OPT,resUR26102OPT,resUR27102OPT,resUR28102OPT,resUR29102OPT,resUR36102OPT,resUR37102OPT,resUR38102OPT,resUR39102OPT,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])] }) min.vector <- unlist(min.list) bestalgo<-res[min.vector,] bestalgo.split <- split(1:nrow(bestalgo), bestalgo$instance, drop=TRUE) for (i in (1:length(bestalgo.split))) { bestalgo.vector <- unlist(bestalgo.split[i]) bestalgo.temp <- bestalgo[bestalgo.vector,] l<-split(bestalgo.temp$best,bestalgo.temp$idalgo) epsfile=paste(linstance[i],"_100_nolim.eps",sep="") postscript(file=epsfile,onefile=TRUE,horizontal=TRUE) par(mar=c(5,5,5,3),cex.axis=0.7,las=2,mgp=c(4, 1, 0)) title_plot=paste("100 iterations - instance ",linstance[i],sep="") boxplot(l,xlab="",ylab="solution value",names=c(levels(bestalgo$idalgo)),main=title_plot,yaxt="n",ylim=c(optimal_values[optimal_values$instance==linstance[i],]$optimum,max(bestalgo.temp$best))) axis(2, seq(from=optimal_values[optimal_values$instance==linstance[i],]$optimum,to=max(bestalgo.temp$best),length.out=10)) abline(h=optimal_values[optimal_values$instance==linstance[i],]$optimum) # draw an orizontal line at the y-level of the best-know solution value grid(nx=0, ny=55,col="gray5") dev.off() }