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mergealldata.jl
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mergealldata.jl
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using MAT
mergemoredata = false;
mergecpdata = true;
function extractfrommat(basename,probsizes,algnames)
iterarrs = Array{Array{Int,2}}(0)
fevarrs = Array{Array{Int,2}}(0)
for probname in sort(collect(keys(probsizes)))
for probsize in probsizes[probname]
MATfile = matopen(basename*probname*"$(probsize).mat")
iters = read(MATfile,"iters")
evals = read(MATfile,"evals")
push!(iterarrs, hcat(collect(Int.(iters[t]) for t in algnames)...))
push!(fevarrs, hcat(collect(Int.(evals[t]) for t in algnames)...))
end
end
iters = vcat(iterarrs...)
fevs = vcat(fevarrs...)
return iters, fevs
end
function writefile(data, fname,algnames)
fid = open(fname,"w")
writedlm(fid, reshape(algnames,1,length(algnames)), ',')
writedlm(fid, data, ',')
close(fid)
end
function extractfromcsv(name)
readdlm(name,',',header=true)[1]
end
"Extract mat data and create csv files"
function createcsvs(basedir, basename, probsizes, algnames, outname)
# TODO: could just loop over all mat-files in data/ ?
#probnames = ["A","B","C","D","E","F","G"]
iters, fevs = extractfrommat(basename,probsizes,algnames)
writefile(iters,basedir*"/iters_$outname.csv",algnames)
writefile(fevs,basedir*"/fevs_$outname.csv",algnames)
return iters, fevs
end
"Handle the Moré et al. data"
function mergemoreprobs(basedir="data", basename=basedir*"/problem",
probsizes = Dict("A" => [100,200], "B" => [100,200], "C" => [100,200],
"D" => [500,1000,50000,100000], "E" => [100,200,50000,100000],
"F" => [200,500], "G" => [100,200]),
algnames = ["ngmreso_sd", "ngmreso_sdls", "ncg", "lbfgs", "ngmres_sd", "ngmres_sdls"],
outname = "AG")
createcsvs(basedir, basename, probsizes, algnames, outname)
end
"Handle tensor CP problem data"
function mergecpprobs(basedir="data", basename=basedir*"/",
probsizes = Dict("tensor_" => ["CP",]),
algnames = ["als", "ngmreso_als", "ncg", "lbfgs", "ngmres_als"],
outname = "tensor_CP")
createcsvs(basedir, basename, probsizes, algnames, outname)
end
using Plots
using BenchmarkProfiles
pgfplots()
#pyplot()
#### Create performance profiles for Moré et al. problems
if mergemoredata == true
# iters = extractfromcsv("data/iters_AG.csv")
# fevs = extractfromcsv("data/fevs_AG.csv")
iters, fevs = mergemoreprobs()
failits = 1500
failidx = find(iters.== failits)
printnames = [ "O-ACCEL-B", "O-ACCEL-A", "N-CG", "L-BFGS", "N-GMRES-B", "N-GMRES-A"]
iters = convert.(Float64,iters)
fevs = convert.(Float64,fevs)
iters[failidx] .= -1
fevs[failidx] .= -1
(ratios,max_ratio) = performance_ratios(fevs; logscale = false)
# plt1 = performance_profile(iters, printnames, title="Iterations")
# plt2 = performance_profile(fevs, printnames, title="f/g evaluations")
plt = performance_profile(fevs,printnames;title="\$f/g\$ evaluations",
logscale=false,
sampletol = 1.5e-2,
xscale=:log2,
xlims=(1,2^3.5),
ylims=(0,1));
algs = [1,5]
pltsd = performance_profile(fevs[:,algs],printnames[algs];title="\$f/g\$ evaluations",
logscale=false,
sampletol = 1e-2,
xscale=:log2, xlims=(1,5),
ylims=(0,1));
algs = [2,6]
pltls = performance_profile(fevs[:,algs],printnames[algs],title="\$f/g\$ evaluations";
logscale=false,
sampletol = 1e-2,
xscale=:log2, xlims=(1,6),
ylims=(0,1));
savefig(plt,"data/perf_prof_all.tex")
savefig(pltsd,"data/perf_prof_nsd.tex")
savefig(pltls,"data/perf_prof_nls.tex")
end
#### Create performance profiles for tensor CP problem
if mergecpdata == true
# iters = extractfromcsv("data/iters_AG.csv")
# fevs = extractfromcsv("data/fevs_AG.csv")
iters, fevs = mergecpprobs()
failits = 1500
failidx = find(iters.== failits)
printnames = ["ALS", "O-ACCEL-ALS", "N-CG", "L-BFGS", "N-GMRES-ALS"]
iters = convert.(Float64,iters)
fevs = convert.(Float64,fevs)
iters[failidx] .= -1
fevs[failidx] .= -1
(ratios,max_ratio) = performance_ratios(fevs; logscale = false)
# plt1 = performance_profile(iters, printnames, title="Iterations")
# plt2 = performance_profile(fevs, printnames, title="f/g evaluations")
plt = performance_profile(fevs,printnames;title="\$f/g\$ evaluations",
logscale=false,
sampletol = 1.5e-2,
xscale=:log2,
#xlims=(1,2^3.5),
ylims=(0,1));
algs = [2,5]
pltacc = performance_profile(fevs[:,algs],printnames[algs];title="\$f/g\$ evaluations",
logscale=false,
sampletol = 1e-2,
xscale=:log2, #xlims=(1,5),
ylims=(0,1));
savefig(plt,"data/perf_prof_tensor_cp_all.tex")
savefig(pltacc,"data/perf_prof_tensor_cp_acc.tex")
end