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build_csv.jl
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build_csv.jl
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# Build tables (csv files) comparing results from the different solvers
using CSV
using DataFrames
function build_non_grouped_csv(corr = true)
modes = ["boscia", "FrankWolfe", "scip", "hypatia", "pajarito"]
type = corr ? "correlated" : "independent"
# prelimenary
#dimensions = [20,30,50,60,80,100,120,150,180,200]
#fracs = vcat(fill(4,5), fill(6,5), fill(8,5), fill(10,5))
# exclusive
dimensions = [50,60,80,100,120]
fracs = vcat(fill(4,5), fill(10,5))
#criteria = corr ? ["DF", "AF", "D"] : ["A", "AF", "D", "DF"] # "DF" independent -> missing SCIP entries, "A" correlated missing Boscia entries
criteria = ["A","AF","D","DF"]
#criteria = ["A"]
for criterion in criteria
@show criterion
println("\n")
df = DataFrame()
df_condi = if criterion in ["AF","DF"]
DataFrame(CSV.File(joinpath(@__DIR__, "csv/fusion_" * type * "_data.csv")))
else
DataFrame(CSV.File(joinpath(@__DIR__, "csv/opt_" * type * "_data.csv")))
end
timeBoscia = []
solutionBoscia = []
terminationBoscia = []
dualGapBoscia = []
relDualGapBoscia = []
lbBoscia = []
numberNodesBoscia = []
timeCustomBB = []
solutionCustomBB = []
terminationCustomBB = []
dualGapCustomBB = []
relDualGapCustomBB = []
numberNodesCustomBB = []
timeScip = []
solutionScip = []
terminationScip = []
dualGapScip = []
relDualGapScip = []
numberCutsScip = []
timePajarito = []
solutionPajarito = []
terminationPajarito = []
dualGapPajarito = []
relDualGapPajarito = []
numberCutsPajarito = []
numberIterPajarito = []
for m in dimensions
@show m
df_dim = DataFrame()
# load boscia
println("Load boscia")
df_boscia = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Boscia/boscia_" * criterion * "_" * string(m) * "_" * type * "_optimality.csv")))
df_boscia.termination .= replace.(df_boscia.termination, "Time limit reached" => "TIME_LIMIT")
for row in eachrow(df_boscia)
if row.time > 3600
row.termination = "TIME_LIMIT"
end
end
println("Termination boscia")
termination_boscia = [row == "OPTIMAL" || row == "tree.lb>primal-dual_gap" || row == "primal>=tree.incumbent" ? 1 : 0 for row in df_boscia[!,:termination]]
println("Set up data")
df_dim[!,:seed] = df_boscia[!,:seed]
df_dim[!,:numberOfExperiments] = df_boscia[!,:numberOfExperiments]
df_dim[!,:numberOfParameters] = df_boscia[!, :numberOfParameters]
df_dim[!,:numberOfAllowedEx] = df_boscia[!,:N]
df_dim[!,:frac] = fracs
println("Reading boscia data")
df_dim[!,:timeBoscia] = df_boscia[!,:time]
timeBoscia = vcat(timeBoscia, df_boscia[!,:time])
df_dim[!,:solutionBoscia] = df_boscia[!,:scaled_solution]
solutionBoscia = vcat(solutionBoscia, df_boscia[!,:scaled_solution])
df_dim[!,:terminationBoscia] = termination_boscia
terminationBoscia=vcat(terminationBoscia, termination_boscia)
lowerBounds = m * (df_boscia[!, :solution] - df_boscia[!, :dual_gap])
df_dim[!,:lbBoscia] = lowerBounds
lbBoscia = vcat(lbBoscia, lowerBounds)
df_dim[!, :dualGapBoscia] = m * df_boscia[!, :dual_gap]
dualGapBoscia = vcat(dualGapBoscia, df_dim[!,:dualGapBoscia])
df_dim[!,:numberNodesBoscia] = df_boscia[!,:num_nodes]
numberNodesBoscia = vcat(numberNodesBoscia, df_boscia[!,:num_nodes])
@show size(df_dim)
# load scip
# only works with the fusion problems
if criterion == "DF" || criterion == "AF"
df_scip = DataFrame(CSV.File(joinpath(@__DIR__, "csv/SCIP/scip_" * criterion * "_" * string(m) * "_" * type * "_optimality.csv")))
df_scip.termination .= replace.(df_scip.termination, "Time limit reached" => "TIME_LIMIT")
for row in eachrow(df_scip)
if row.time > 3600
row.termination = "TIME_LIMIT"
end
end
termination_scip = [row == "OPTIMAL" ? 1 : 0 for row in df_scip[!,:termination]]
println("Load SCIP")
df_scip[!,:timeScip] = df_scip[!,:time]
timeScip = vcat(timeScip, df_scip[!,:time])
df_scip[!,:terminationScip] = termination_scip
terminationScip = vcat(terminationScip, termination_scip)
df_scip[!,:solutionScip] = df_scip[!,:solution]*m
solutionScip = vcat(solutionScip, df_scip[!,:solution]*m)
df_scip[!,:dualGapScip] = df_scip[!, :solution]*m - lowerBounds
dualGapScip = vcat(dualGapScip, df_scip[!,:dualGapScip])
df_scip[!,:numberCutsScip] = df_scip[!,:calls]
numberCutsScip = vcat(numberCutsScip, df_scip[!,:calls])
df_scip[!,:frac] = fracs
df_scip[!,:numberOfAllowedEx] = df_scip[!,:N]
df_scip = select(df_scip, [:terminationScip, :timeScip, :solutionScip, :dualGapScip, :numberCutsScip, :seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
df_dim = outerjoin(df_dim, df_scip, on = [:seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
@show size(df_dim)
#= else
df_bb = DataFrame(CSV.File(joinpath(@__DIR__, "csv/CustomBB/customBB_" * criterion * "_" * string(m) * "_" * type * "_optimality.csv")))
df_bb.termination .= replace.(df_bb.termination, "Time limit reached" => "TIME_LIMIT")
for row in eachrow(df_bb)
if row.time > 3600
row.termination = "TIME_LIMIT"
end
end
termination_bb = [row == "optimal" ? 1 : 0 for row in df_bb[!,:termination]]
println("Load Custom BB")
df_bb[!,:timeCustomBB] = df_bb[!,:time]
timeCustomBB = vcat(timeCustomBB, df_bb[!,:time])
df_bb[!,:terminationCustomBB] = termination_bb
terminationCustomBB = vcat(terminationCustomBB, termination_bb)
df_bb[!,:solutionCustomBB] = df_bb[!,:solution_scaled]
solutionCustomBB = vcat(solutionCustomBB, df_bb[!,:solution_scaled])
df_bb[!,:dualGapCustomBB] = df_bb[!, :solution_scaled] - lowerBounds
dualGapCustomBB = vcat(dualGapCustomBB, df_bb[!,:dualGapCustomBB])
df_bb[!,:numberNodesCustomBB] = df_bb[!,:number_nodes]
numberNodesCustomBB = vcat(numberNodesCustomBB, df_bb[!,:number_nodes])
df_bb[!,:frac] = fracs
df_bb[!,:numberOfAllowedEx] = df_bb[!,:N]
df_bb = select(df_bb, [:terminationCustomBB, :timeCustomBB, :solutionCustomBB, :dualGapCustomBB, :numberNodesCustomBB, :seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
@show size(df_bb)
df_dim = outerjoin(df_dim, df_bb, on = [:seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
@show size(df_dim)=#
end
df_bb = DataFrame(CSV.File(joinpath(@__DIR__, "csv/CustomBB/customBB_" * criterion * "_" * string(m) * "_" * type * "_optimality.csv")))
df_bb.termination .= replace.(df_bb.termination, "Time limit reached" => "TIME_LIMIT")
for row in eachrow(df_bb)
if row.time > 3600
row.termination = "TIME_LIMIT"
end
end
termination_bb = [row == "optimal" ? 1 : 0 for row in df_bb[!,:termination]]
println("Load Custom BB")
df_bb[!,:timeCustomBB] = df_bb[!,:time]
timeCustomBB = vcat(timeCustomBB, df_bb[!,:time])
df_bb[!,:terminationCustomBB] = termination_bb
terminationCustomBB = vcat(terminationCustomBB, termination_bb)
df_bb[!,:solutionCustomBB] = df_bb[!,:solution_scaled]
solutionCustomBB = vcat(solutionCustomBB, df_bb[!,:solution_scaled])
df_bb[!,:dualGapCustomBB] = df_bb[!, :solution_scaled] - lowerBounds
dualGapCustomBB = vcat(dualGapCustomBB, df_bb[!,:dualGapCustomBB])
df_bb[!,:numberNodesCustomBB] = df_bb[!,:number_nodes]
numberNodesCustomBB = vcat(numberNodesCustomBB, df_bb[!,:number_nodes])
df_bb[!,:frac] = fracs
df_bb[!,:numberOfAllowedEx] = df_bb[!,:N]
df_bb = select(df_bb, [:terminationCustomBB, :timeCustomBB, :solutionCustomBB, :dualGapCustomBB, :numberNodesCustomBB, :seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
@show size(df_bb)
df_dim = outerjoin(df_dim, df_bb, on = [:seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
@show size(df_dim)
# load pajarito
println("Load Pajarito")
df_paj = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Pajarito/pajarito_" * criterion * "_" * string(m) * "_" * type * "_optimality.csv")))
for row in eachrow(df_paj)
if row.time > 3600
row.termination = "TIME_LIMIT"
end
end
termination_paj = [row == "OPTIMAL" ? 1 : 0 for row in df_paj[!,:termination]]
time_paj = df_paj[!,:time]
for i in eachindex(time_paj)
if df_paj[i,:termination] == "OTHER_ERROR"
time_paj[i] = 3600.00
end
end
df_paj[!,:timePajarito] = time_paj
timePajarito = vcat(timePajarito, time_paj)
df_paj[!,:solutionPajarito] = df_paj[!,:solution]
solutionPajarito = vcat(solutionPajarito, df_paj[!,:solution])
df_paj[!,:terminationPajarito] = termination_paj
terminationPajarito = vcat(terminationPajarito, termination_paj)
df_paj[!,:dualGapPajarito] = df_paj[!, :solution] - lowerBounds
dualGapPajarito = vcat(dualGapPajarito, df_paj[!,:dualGapPajarito])
df_paj[!,:numberCutsPajarito] = df_paj[!,:numberCuts]
numberCutsPajarito = vcat(numberCutsPajarito, df_paj[!,:numberCuts])
df_paj[!,:numberIterPajarito] = df_paj[!,:numberIterations]
numberIterPajarito = vcat(numberIterPajarito, df_paj[!,:numberIterations])
df_paj[!,:frac] = fracs
df_paj[!,:numberOfAllowedEx] = df_paj[!,:N]
df_paj = select(df_paj, [:terminationPajarito, :timePajarito, :solutionPajarito, :dualGapPajarito, :numberCutsPajarito, :numberIterPajarito, :seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
df_dim = outerjoin(df_dim, df_paj, on = [:seed, :numberOfExperiments, :numberOfParameters, :numberOfAllowedEx, :frac])
@show size(df_dim)
# compute relative gap
rel_gap_scip = []
rel_gap_boscia = []
rel_gap_pajarito = []
rel_gap_custombb = []
#rel_gap_hypatia_cont = []
#rel_gap_hypatia_limit = []
#rel_gap_frankwolfe_cont = []
#rel_gap_frankwolfe_limit = []
for row in eachrow(df_dim)
if criterion == "AF" || criterion == "DF"
if min(abs(row.solutionScip), abs(row.lbBoscia)) == 0
push!(rel_gap_scip, row.solutionScip - row.lbBoscia)
elseif sign(row.lbBoscia) != sign(row.solutionScip)
push!(rel_gap_scip, Inf)
else
push!(rel_gap_scip, (row.solutionScip - row.lbBoscia)/min(abs(row.solutionScip), abs(row.lbBoscia)))
end
# else
# if min(abs(row.solutionCustomBB), abs(row.lbBoscia)) == 0
# push!(rel_gap_custombb, row.solutionCustomBB - row.lbBoscia)
# elseif sign(row.lbBoscia) != sign(row.solutionCustomBB)
# push!(rel_gap_custombb, Inf)
# else
# push!(rel_gap_custombb, (row.solutionCustomBB - row.lbBoscia)/min(abs(row.solutionCustomBB), abs(row.lbBoscia)))
# end
end
if min(abs(row.solutionCustomBB), abs(row.lbBoscia)) == 0
push!(rel_gap_custombb, row.solutionCustomBB - row.lbBoscia)
elseif sign(row.lbBoscia) != sign(row.solutionCustomBB)
push!(rel_gap_custombb, Inf)
else
push!(rel_gap_custombb, (row.solutionCustomBB - row.lbBoscia)/min(abs(row.solutionCustomBB), abs(row.lbBoscia)))
end
if min(abs(row.solutionBoscia), abs(row.lbBoscia)) == 0
push!(rel_gap_boscia, row.solutionBoscia - row.lbBoscia)
elseif sign(row.lbBoscia) != sign(row.solutionBoscia) || row.solutionBoscia == Inf
push!(rel_gap_boscia, Inf)
else
push!(rel_gap_boscia, (row.solutionBoscia - row.lbBoscia)/min(abs(row.solutionBoscia), abs(row.lbBoscia)))
end
if min(abs(row.solutionPajarito), abs(row.lbBoscia)) == 0
push!(rel_gap_pajarito, row.solutionPajarito - row.lbBoscia)
elseif sign(row.lbBoscia) != sign(row.solutionPajarito) || row.solutionPajarito == Inf
push!(rel_gap_pajarito, Inf)
else
push!(rel_gap_pajarito, (row.solutionPajarito - row.lbBoscia)/min(abs(row.solutionPajarito), abs(row.lbBoscia)))
end
end
if criterion == "AF" || criterion == "DF"
df_dim[!, :relGapScip] = round.(rel_gap_scip,digits=3)
relDualGapScip = vcat(relDualGapScip, rel_gap_scip)
# else
# df_dim[!,:relGapCustomBB] = round.(rel_gap_custombb, digits=3)
# relDualGapCustomBB = vcat(relDualGapCustomBB, rel_gap_custombb)
end
df_dim[!,:relGapCustomBB] = round.(rel_gap_custombb, digits=3)
relDualGapCustomBB = vcat(relDualGapCustomBB, rel_gap_custombb)
df_dim[!, :relGapBoscia] = round.(rel_gap_boscia, digits=3)
relDualGapBoscia = vcat(relDualGapBoscia, rel_gap_boscia)
df_dim[!, :relGapPajarito] = round.(rel_gap_pajarito,digits=3)
relDualGapPajarito = vcat(relDualGapPajarito, rel_gap_pajarito)
# df[!, :relGapHypatiaCont] = rel_gap_hypatia_cont
# df[!, :relGapHypatiaLimit] = rel_gap_hypatia_limit
# df[!, :relGapFrankWolfeCont] = rel_gap_frankwolfe_cont
# df[!, :relGapFrankWolfeLimit] = rel_gap_frankwolfe_limit
# save csv
file_name = joinpath(@__DIR__, "csv/Results/" * criterion * "_" * string(m) * "_optimality_" * type * "_non_grouped.csv")
CSV.write(file_name, df_dim, append=false)
println("\n")
end
println("Build complete non grouped csv")
df[!,:seed] = df_condi[!,:seed]
df[!,:numberOfExperiments] = df_condi[!,:numberOfExperiments]
df[!,:numberOfParameters] = df_condi[!, :numberOfParameters]
df[!,:numberOfAllowedEx] = df_condi[!,:numberOfAllowedEx]
df[!,:frac] = df_condi[!,:frac]
df[!,:eigmax] = df_condi[!,:eigmax]
df[!,:eigmin] = df_condi[!,:eigmin]
df[!,:ratio] = df_condi[!,:ratio]
df[!,:timeBoscia] = timeBoscia
df[!,:solutionBoscia] = solutionBoscia
df[!,:terminationBoscia] = terminationBoscia
df[!,:dualGapBoscia] = dualGapBoscia
df[!,:relDualGapBoscia] = relDualGapBoscia
df[!,:lbBoscia] = lbBoscia
df[!,:numberNodesBoscia] = numberNodesBoscia
if criterion in ["AF","DF"]
df[!,:timeScip] = timeScip
df[!,:solutionScip] = solutionScip
df[!,:terminationScip] = terminationScip
df[!,:dualGapScip] = dualGapScip
df[!,:relDualGapScip] = relDualGapScip
df[!,:numberCutsScip] = numberCutsScip
# else
# df[!,:timeCustomBB] = timeCustomBB
# df[!,:solutionCustomBB] = solutionCustomBB
# df[!,:terminationCustomBB] = terminationCustomBB
# df[!,:dualGapCustomBB] = dualGapCustomBB
# df[!,:relDualGapCustomBB] = relDualGapCustomBB
# df[!,:numberNodesCustomBB] = numberNodesCustomBB
end
df[!,:timeCustomBB] = timeCustomBB
df[!,:solutionCustomBB] = solutionCustomBB
df[!,:terminationCustomBB] = terminationCustomBB
df[!,:dualGapCustomBB] = dualGapCustomBB
df[!,:relDualGapCustomBB] = relDualGapCustomBB
df[!,:numberNodesCustomBB] = numberNodesCustomBB
df[!,:timePajarito] = timePajarito
df[!,:solutionPajarito] = solutionPajarito
df[!,:terminationPajarito] = terminationPajarito
df[!,:dualGapPajarito] = dualGapPajarito
df[!,:relDualGapPajarito] = relDualGapPajarito
df[!,:numberCutsPajarito] = numberCutsPajarito
df[!,:numberIterPajarito] = numberIterPajarito
if criterion in ["AF", "DF"]
df[!,:minimumTime] = min.(df[!,:timeBoscia], df[!,:timeScip], df[!,:timePajarito])
# else
# df[!,:minimumTime] = min.(df[!,:timeBoscia], df[!,:timePajarito], df[!,:timeCustomBB])
end
df[!,:minimumTime] = min.(df[!,:timeBoscia], df[!,:timePajarito], df[!,:timeCustomBB])
file_name = joinpath(@__DIR__, "csv/Results/" * criterion * "_optimality_" * type * "_non_grouped.csv")
CSV.write(file_name, df, append=false)
println("\n")
end
end
function build_grouped_csv(corr = true)
function geo_mean(group)
prod = 1.0
n = 0
for element in group
# @show element
if element != Inf
prod = prod * element
n += 1
end
end
@show prod, n
if n == 0
return Inf
end
return prod^(1/n)
end
function custom_mean(group)
sum = 0.0
n = 0
dash = false
for element in group
if element == "-"
dash = true
continue
end
if element != Inf
if typeof(element) == String7 || typeof(element) == String3
element = parse(Float64, element)
end
sum += element
n += 1
end
end
if n == 0
return dash ? "-" : Inf
end
return sum/n
end
#= function geom_shifted_mean(xs; shift=big"1.0")
n = length(xs)
if n != 0
r = prod(xi + shift for xi in xs)
return Float64(r^(1/n) - shift)
end
return Inf
end=#
function geom_shifted_mean(xs; shift=big"1.0")
a = length(xs)
n= 0
prod = 1.0
dash = false
if a != 0
for xi in xs
if xi == "-"
dash = true
continue
end
if xi != Inf && xi != "-"
prod = prod*(xi+shift)
n += 1
end
end
return Float64(prod^(1/n) - shift)
end
return dash ? "-" : Inf
end
#criteria = corr ? ["DF", "AF", "D"] : ["A", "AF", "D", "DF"] # "A" correlated
#criteria=["A"]
criteria = ["A","AF","D","DF"]
for criterion in criteria
@show criterion
type = corr ? "correlated" : "independent"
df = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/" * criterion *"_optimality_" * type * "_non_grouped.csv")))
println("Clean up Time")
# set time below 1800
df[df.timeBoscia.>3600, :timeBoscia] .= 3600
#df[df.timeFrankWolfeCont.>1800, :timeFrankWolfeCont] .= 1800
#df[df.timeFrankWolfeLimit.>1800, :timeFrankWolfeLimit] .= 1800
if criterion == "AF" || criterion == "DF"
df[df.timeScip.>3600, :timeScip] .= 3600
else
df[df.timeCustomBB.>3600, :timeCustomBB] .= 3600
end
#df[df.timeHypatiaCont.>1800, :timeHypatiaCont] .= 1800
#df[df.timeHypatiaLimit.>1800, :timeHypatiaLimit] .= 1800
df[df.timePajarito.>3600, :timePajarito] .= 3600
println("Combine data")
if criterion == "AF" || criterion == "DF"
gdf = combine(
groupby(df, [:numberOfExperiments, :numberOfParameters, :frac]),
:eigmax => custom_mean, :eigmin => custom_mean, :ratio => custom_mean,
:timeBoscia => geom_shifted_mean, :terminationBoscia => sum,
:relDualGapBoscia => custom_mean, :dualGapBoscia => custom_mean, # maybe normal mean?
:numberNodesBoscia => custom_mean,
# :timeFrankWolfeCont => geom_shifted_mean, :terminationFrankWolfeCont => sum,
# :relGapFrankWolfeCont => custom_mean, :dualGapFrankWolfeCont => custom_mean,
# :intFoundFrankWolfeCont => sum,
# :timeFrankWolfeLimit => geom_shifted_mean, :terminationFrankWolfeLimit => sum,
# :relGapFrankWolfeLimit => custom_mean, :dualGapFrankWolfeLimit => custom_mean,
# :intFoundFrankWolfeLimit => sum,
:timeScip => geom_shifted_mean, :terminationScip => sum,
:relDualGapScip => custom_mean, :dualGapScip => custom_mean,
:numberCutsScip => custom_mean,
:timeCustomBB => geom_shifted_mean, :terminationCustomBB => sum,
:relDualGapCustomBB => custom_mean, :dualGapCustomBB => custom_mean,
:numberNodesCustomBB => custom_mean,
#:timeHypatiaCont => geom_shifted_mean, :terminationHypatiaCont => sum,
#:relGapHypatiaCont => custom_mean, :dualGapHypatiaCont => custom_mean,
#:intFoundHypatiaCont => sum,
#:timeHypatiaLimit => geom_shifted_mean, :terminationHypatiaLimit => sum,
#:relGapHypatiaLimit => custom_mean, :dualGapHypatiaLimit => custom_mean,
#:intFoundHypatiaLimit => sum,
:timePajarito => geom_shifted_mean, :terminationPajarito => sum,
:relDualGapPajarito => custom_mean, :dualGapPajarito => custom_mean,
:numberCutsPajarito => custom_mean, :numberIterPajarito => custom_mean,
nrow => :NumInstances, renamecols=false
)
else
gdf = combine(
groupby(df, [:numberOfExperiments, :numberOfParameters, :frac]),
:eigmax => custom_mean, :eigmin => custom_mean, :ratio => custom_mean,
:timeBoscia => geom_shifted_mean, :terminationBoscia => sum,
:relDualGapBoscia => custom_mean, :dualGapBoscia => custom_mean, # maybe normal mean?
:numberNodesBoscia => custom_mean,
#:timeFrankWolfeCont => geom_shifted_mean, :terminationFrankWolfeCont => sum,
#:relGapFrankWolfeCont => custom_mean, :dualGapFrankWolfeCont => custom_mean,
#:intFoundFrankWolfeCont => sum,
#:timeFrankWolfeLimit => geom_shifted_mean, :terminationFrankWolfeLimit => sum,
#:relGapFrankWolfeLimit => custom_mean, :dualGapFrankWolfeLimit => custom_mean,
#:intFoundFrankWolfeLimit => sum,
:timeCustomBB => geom_shifted_mean, :terminationCustomBB => sum,
:relDualGapCustomBB => custom_mean, :dualGapCustomBB => custom_mean,
:numberNodesCustomBB => custom_mean,
#:timeHypatiaCont => geom_shifted_mean, :terminationHypatiaCont => sum,
#:relGapHypatiaCont => custom_mean, :dualGapHypatiaCont => custom_mean,
#:intFoundHypatiaCont => sum,
#:timeHypatiaLimit => geom_shifted_mean, :terminationHypatiaLimit => sum,
#:relGapHypatiaLimit => custom_mean, :dualGapHypatiaLimit => custom_mean,
#:intFoundHypatiaLimit => sum,
:timePajarito => geom_shifted_mean, :terminationPajarito => sum,
:relDualGapPajarito => custom_mean, :dualGapPajarito => custom_mean,
:numberCutsPajarito => custom_mean, :numberIterPajarito => custom_mean,
nrow => :NumInstances, renamecols=false
)
end
println("Clean up and rounding")
gdf[!,:ratio] = round.(gdf[!,:ratio], digits=2)
gdf[!,:timeBoscia] = convert.(Int64,round.(gdf[!,:timeBoscia]))
#gdf[!,:timeFrankWolfeCont] = convert.(Int64,round.(gdf[!,:timeFrankWolfeCont]))
#gdf[!,:timeFrankWolfeLimit] = convert.(Int64,round.(gdf[!,:timeFrankWolfeLimit]))
if criterion == "AF" || criterion == "DF"
gdf[!,:timeScip] = convert.(Int64,round.(gdf[!,:timeScip]))
# else
# gdf[!,:timeCustomBB] = convert.(Int64, round.(gdf[!,:timeCustomBB]))
end
gdf[!,:timeCustomBB] = convert.(Int64, round.(gdf[!,:timeCustomBB]))
#gdf[!,:timeHypatiaCont] = convert.(Int64,round.(gdf[!,:timeHypatiaCont]))
#gdf[!,:timeHypatiaLimit] = convert.(Int64,round.(gdf[!,:timeHypatiaLimit]))
gdf[!,:timePajarito] = convert.(Int64,round.(gdf[!,:timePajarito]))
# relative instances solved
gdf[!,:terminationBosciaRel] = gdf[!,:terminationBoscia]./gdf[!,:NumInstances]*100
# gdf[!,:terminationFWContRel] = gdf[!,:terminationFrankWolfeCont]./gdf[!,:NumInstances]*100
# gdf[!,:terminationFWLimitRel] = gdf[!,:terminationFrankWolfeLimit]./gdf[!,:NumInstances]*100
if criterion == "AF" || criterion == "DF"
gdf[!,:terminationScipRel] = gdf[!,:terminationScip]./gdf[!,:NumInstances]*100
# else
# gdf[!,:terminationCustomBBRel] = gdf[!,:terminationCustomBB]./gdf[!,:NumInstances]*100
end
gdf[!,:terminationCustomBBRel] = gdf[!,:terminationCustomBB]./gdf[!,:NumInstances]*100
#gdf[!,:terminationHypatiaContRel] = gdf[!,:terminationHypatiaCont]./gdf[!,:NumInstances]*100
#gdf[!,:terminationHypatiaLimitRel] = gdf[!,:terminationHypatiaLimit]./gdf[!,:NumInstances]*100
gdf[!,:terminationPajaritoRel] = gdf[!,:terminationPajarito]./gdf[!,:NumInstances]*100
# relative integer solutions found
#gdf[!, :intFoundHypatiaContRel] = gdf[!,:intFoundHypatiaCont]./gdf[!,:NumInstances]*100
#gdf[!, :intFoundHypatiaLimitRel] = gdf[!,:intFoundHypatiaLimit]./gdf[!,:NumInstances]*100
#gdf[!, :intFoundFrankWolfeContRel] = gdf[!,:intFoundFrankWolfeCont]./gdf[!,:NumInstances]*100
#gdf[!, :intFoundFrankWolfeLimitRel] = gdf[!,:intFoundFrankWolfeLimit]./gdf[!,:NumInstances]*100
# parse to int
gdf[!,:terminationBosciaRel] = convert.(Int64,round.(gdf[!,:terminationBosciaRel]))
# gdf[!,:terminationFWContRel] = convert.(Int64,round.(gdf[!,:terminationFWContRel]))
# gdf[!,:terminationFWLimitRel] = convert.(Int64,round.(gdf[!,:terminationFWLimitRel]))
if criterion == "AF" || criterion == "DF"
gdf[!,:terminationScipRel] = convert.(Int64,round.(gdf[!,:terminationScipRel]))
# else
# gdf[!,:terminationCustomBBRel] = convert.(Int64,round.(gdf[!,:terminationCustomBBRel]))
end
gdf[!,:terminationCustomBBRel] = convert.(Int64,round.(gdf[!,:terminationCustomBBRel]))
#gdf[!,:terminationHypatiaContRel] = convert.(Int64,round.(gdf[!,:terminationHypatiaContRel]))
#gdf[!,:terminationHypatiaLimitRel] = convert.(Int64,round.(gdf[!,:terminationHypatiaLimitRel]))
gdf[!,:terminationPajaritoRel] = convert.(Int64,round.(gdf[!,:terminationPajaritoRel]))
# Additional Data like number of nodes/custom_mean
non_inf_entries = findall(isfinite, gdf[!,:numberNodesBoscia])
gdf[non_inf_entries,:numberNodesBoscia] = convert.(Int64, round.(gdf[non_inf_entries, :numberNodesBoscia]))
if criterion in ["AF","DF"]
non_inf_entries = findall(isfinite, gdf[!,:numberCutsScip])
gdf[non_inf_entries,:numberCutsScip] = convert.(Int64, round.(gdf[non_inf_entries, :numberCutsScip]))
# else
# non_inf_entries = findall(isfinite, gdf[!,:numberNodesCustomBB])
# gdf[non_inf_entries,:numberNodesCustomBB] = convert.(Int64, round.(gdf[non_inf_entries, :numberNodesCustomBB]))
end
non_inf_entries = findall(isfinite, gdf[!,:numberNodesCustomBB])
gdf[non_inf_entries,:numberNodesCustomBB] = convert.(Int64, round.(gdf[non_inf_entries, :numberNodesCustomBB]))
non_inf_entries = findall(isfinite, gdf[!,:numberCutsPajarito])
gdf[non_inf_entries,:numberCutsPajarito] = convert.(Int64, round.(gdf[non_inf_entries, :numberCutsPajarito]))
non_inf_entries = findall(isfinite, gdf[!,:numberIterPajarito])
gdf[non_inf_entries,:numberIterPajarito] = convert.(Int64, round.(gdf[non_inf_entries, :numberIterPajarito]))
#gdf[!,:dualGapFrankWolfeCont] = round.(gdf[!,:dualGapFrankWolfeCont], digits=2)
#gdf[!,:dualGapFrankWolfeLimit] = round.(gdf[!,:dualGapFrankWolfeLimit], digits=2)
#gdf[!,:dualGapHypatiaCont] = round.(gdf[!,:dualGapHypatiaCont], digits=2)
#gdf[!,:dualGapHypatiaLimit] = round.(gdf[!,:dualGapHypatiaLimit], digits=2)
#gdf[!,:intFoundFrankWolfeContRel] = round.(gdf[!,:intFoundFrankWolfeContRel], digits=2)
#gdf[!,:intFoundFrankWolfeLimitRel] = round.(gdf[!,:intFoundFrankWolfeLimitRel], digits=2)
#gdf[!,:intFoundHypatiaContRel] = round.(gdf[!,:intFoundHypatiaContRel], digits=2)
#gdf[!,:intFoundHypatiaLimitRel] = round.(gdf[!,:intFoundHypatiaLimitRel], digits=2)
non_inf_entries = findall(isfinite, gdf[!, :relDualGapBoscia])
gdf[non_inf_entries, :relDualGapBoscia] = convert.(Int64, round.(gdf[non_inf_entries, :relDualGapBoscia]*100))
non_inf_entries = findall(isfinite, gdf[!, :relDualGapPajarito])
gdf[non_inf_entries, :relDualGapPajarito] = convert.(Int64, round.(gdf[non_inf_entries, :relDualGapPajarito]*100))
#non_inf_entries = findall(x-> x!= "-" && x!=Inf, gdf[!, :relGapHypatiaCont])
#gdf[non_inf_entries, :relGapHypatiaCont] = convert.(Int64, round.(gdf[non_inf_entries, :relGapHypatiaCont]*100))
#non_inf_entries = findall(x->x!="-" && x!=Inf, gdf[!, :relGapHypatiaLimit])
#gdf[non_inf_entries, :relGapHypatiaLimit] = convert.(Int64, round.(gdf[non_inf_entries, :relGapHypatiaLimit]*100))
#non_inf_entries = findall(x->x!="-" && x!=Inf, gdf[!, :relGapFrankWolfeCont])
#gdf[non_inf_entries, :relGapFrankWolfeCont] = convert.(Int64, round.(gdf[non_inf_entries, :relGapFrankWolfeCont]*100))
#non_inf_entries = findall(x->x!="-" && x!=Inf, gdf[!, :relGapFrankWolfeLimit])
#gdf[non_inf_entries, :relGapFrankWolfeLimit] = convert.(Int64, round.(gdf[non_inf_entries, :relGapFrankWolfeLimit]*100))
if criterion == "AF" || criterion == "DF"
non_inf_entries = findall(isfinite, gdf[!, :relDualGapScip])
gdf[non_inf_entries, :relDualGapScip] = convert.(Int64, round.(gdf[non_inf_entries, :relDualGapScip]*100))
# else
# non_inf_entries = findall(isfinite, gdf[!, :relDualGapCustomBB])
# gdf[non_inf_entries, :relDualGapCustomBB] = convert.(Int64, round.(gdf[non_inf_entries, :relDualGapCustomBB]*100))
end
non_inf_entries = findall(isfinite, gdf[!, :relDualGapCustomBB])
gdf[non_inf_entries, :relDualGapCustomBB] = convert.(Int64, round.(gdf[non_inf_entries, :relDualGapCustomBB]*100))
file_name = joinpath(@__DIR__, "csv/Results/" * criterion * "_optimality_" * type * "_grouped.csv")
CSV.write(file_name, gdf, append=false)
end
return true
end
# #of instances #solved #avg time total #avg time solved #Nodes/Cuts #rel gap in case not solved
function build_summary_by_criterion()
function geo_mean(group)
prod = 1.0
n = 0
if isempty(group)
return -1
end
for element in group
# @show element
if element != Inf
prod = prod * abs(element)
n += 1
end
end
@show prod, n
if n == 0
return Inf
end
return prod^(1/n)
end
function geom_shifted_mean(xs; shift=big"1.0")
a = length(xs)
n= 0
prod = 1.0
if a != 0
for xi in xs
if xi != Inf
prod = prod*(xi+shift)
n += 1
end
end
return Float64(prod^(1/n) - shift)
end
return Inf
end
function custom_mean(group)
sum = 0.0
n = 0
dash = false
if isempty(group)
return -1
end
for element in group
if element == "-"
dash = true
continue
end
if element != Inf
if typeof(element) == String7 || typeof(element) == String3
element = parse(Float64, element)
end
sum += element
n += 1
end
end
if n == 0
return dash ? "-" : Inf
end
return sum/n
end
df = DataFrame()
NumInstances = 50
# load data
df_AF_ind = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/AF_optimality_independent_non_grouped.csv")))
df_A_ind = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/A_optimality_independent_non_grouped.csv")))
df_DF_ind = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/DF_optimality_independent_non_grouped.csv")))
df_D_ind = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/D_optimality_independent_non_grouped.csv")))
df_AF_corr = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/AF_optimality_correlated_non_grouped.csv")))
df_A_corr = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/A_optimality_correlated_non_grouped.csv")))
df_DF_corr = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/DF_optimality_correlated_non_grouped.csv")))
df_D_corr = DataFrame(CSV.File(joinpath(@__DIR__, "csv/Results/D_optimality_correlated_non_grouped.csv")))
# we are only interested in entries with termination
ind_term_AF_ind_Boscia = findall(x-> x==1, df_AF_ind[!,:terminationBoscia])
ind_term_AF_ind_Scip = findall(x-> x==1, df_AF_ind[!,:terminationScip])
ind_term_AF_ind_Pajarito = findall(x-> x==1, df_AF_ind[!,:terminationPajarito])
ind_term_AF_ind_CustomBB = findall(x->x==1, df_AF_ind[!,:terminationCustomBB])
ind_term_A_ind_Boscia = findall(x-> x==1, df_A_ind[!,:terminationBoscia])
ind_term_A_ind_Pajarito = findall(x-> x==1, df_A_ind[!,:terminationPajarito])
ind_term_A_ind_CustomBB = findall(x->x==1, df_A_ind[!,:terminationCustomBB])
ind_term_DF_ind_Boscia = findall(x-> x==1, df_DF_ind[!,:terminationBoscia])
ind_term_DF_ind_Scip = findall(x-> x==1, df_DF_ind[!,:terminationScip])
ind_term_DF_ind_Pajarito = findall(x-> x==1, df_DF_ind[!,:terminationPajarito])
ind_term_DF_ind_CustomBB = findall(x->x==1, df_DF_ind[!,:terminationCustomBB])
ind_term_D_ind_Boscia = findall(x-> x==1, df_D_ind[!,:terminationBoscia])
ind_term_D_ind_Pajarito = findall(x-> x==1, df_D_ind[!,:terminationPajarito])
ind_term_D_ind_CustomBB = findall(x->x==1, df_D_ind[!,:terminationCustomBB])
ind_term_AF_corr_Boscia = findall(x-> x==1, df_AF_corr[!,:terminationBoscia])
ind_term_AF_corr_Scip = findall(x-> x==1, df_AF_corr[!,:terminationScip])
ind_term_AF_corr_Pajarito = findall(x-> x==1, df_AF_corr[!,:terminationPajarito])
ind_term_AF_corr_CustomBB = findall(x->x==1, df_AF_corr[!,:terminationCustomBB])
ind_term_A_corr_Boscia = findall(x-> x==1, df_A_corr[!,:terminationBoscia])
ind_term_A_corr_Pajarito = findall(x-> x==1, df_A_corr[!,:terminationPajarito])
ind_term_A_corr_CustomBB = findall(x->x==1, df_A_corr[!,:terminationCustomBB])
ind_term_DF_corr_Boscia = findall(x-> x==1, df_DF_corr[!,:terminationBoscia])
ind_term_DF_corr_Scip = findall(x-> x==1, df_DF_corr[!,:terminationScip])
ind_term_DF_corr_Pajarito = findall(x-> x==1, df_DF_corr[!,:terminationPajarito])
ind_term_DF_corr_CustomBB = findall(x->x==1, df_DF_corr[!,:terminationCustomBB])
ind_term_D_corr_Boscia = findall(x-> x==1, df_D_corr[!,:terminationBoscia])
ind_term_D_corr_Pajarito = findall(x-> x==1, df_D_corr[!,:terminationPajarito])
ind_term_D_corr_CustomBB = findall(x->x==1, df_D_corr[!,:terminationCustomBB])
boscia_term = []
scip_term = []
pajarito_term = []
custombb_term = []
boscia_time = []
scip_time = []
pajarito_time = []
custombb_time = []
boscia_time_all = []
scip_time_all = []
pajarito_time_all = []
custombb_time_all = []
boscia_term_rel = []
scip_term_rel = []
pajarito_term_rel = []
custombb_term_rel = []
boscia_nodes = []
custombb_nodes = []
scip_cuts = []
pajarito_cuts= []
pajarito_iters = []
boscia_rel_gap = []
custombb_rel_gap = []
scip_rel_gap = []
pajarito_rel_gap = []
## termination
println("Termination")
push!(boscia_term, length(ind_term_AF_ind_Boscia))
push!(scip_term, length(ind_term_AF_ind_Scip))
push!(pajarito_term, length(ind_term_AF_ind_Pajarito))
# push!(custombb_term, 0)
push!(custombb_term, length(ind_term_AF_ind_CustomBB))
push!(boscia_term, length(ind_term_AF_corr_Boscia))
push!(scip_term, length(ind_term_AF_corr_Scip))
push!(pajarito_term, length(ind_term_AF_corr_Pajarito))
#push!(custombb_term, 0)
push!(custombb_term, length(ind_term_AF_corr_CustomBB))
push!(boscia_term, length(ind_term_A_ind_Boscia))
push!(scip_term, 0)
push!(pajarito_term, length(ind_term_A_corr_Pajarito))
push!(custombb_term, length(ind_term_A_corr_CustomBB))
push!(boscia_term, length(ind_term_A_corr_Boscia))
push!(scip_term, 0)
push!(pajarito_term, length(ind_term_A_corr_Pajarito))
push!(custombb_term, length(ind_term_A_corr_CustomBB))
push!(boscia_term, length(ind_term_DF_ind_Boscia))
push!(scip_term, length(ind_term_DF_ind_Scip))
push!(pajarito_term, length(ind_term_DF_ind_Pajarito))
#push!(custombb_term, 0)
push!(custombb_term, length(ind_term_DF_ind_CustomBB))
push!(boscia_term, length(ind_term_DF_corr_Boscia))
push!(scip_term, length(ind_term_DF_corr_Scip))
push!(pajarito_term, length(ind_term_DF_corr_Pajarito))
#push!(custombb_term, 0)
push!(custombb_term, length(ind_term_DF_corr_CustomBB))
push!(boscia_term, length(ind_term_D_ind_Boscia))
push!(scip_term, 0)
push!(pajarito_term, length(ind_term_D_ind_Pajarito))
push!(custombb_term, length(ind_term_D_ind_CustomBB))
push!(boscia_term, length(ind_term_D_corr_Boscia))
push!(scip_term, 0)
push!(pajarito_term, length(ind_term_D_corr_Pajarito))
push!(custombb_term, length(ind_term_D_corr_CustomBB))
## relative termination
println("Relative termination")
boscia_term_rel = convert.(Int64, round.(boscia_term./NumInstances*100))
scip_term_rel = convert.(Int64, round.(scip_term./NumInstances*100))
pajarito_term_rel = convert.(Int64, round.(pajarito_term./NumInstances*100))
custombb_term_rel = convert.(Int64, round.(custombb_term./NumInstances*100))
## time for solved instances
println("Time solved instances")
push!(boscia_time, geom_shifted_mean(df_AF_ind[ind_term_AF_ind_Boscia,:timeBoscia]))
push!(scip_time, geom_shifted_mean(df_AF_ind[ind_term_AF_ind_Scip,:timeScip]))
push!(pajarito_time, geom_shifted_mean(df_AF_ind[ind_term_AF_ind_Pajarito,:timePajarito]))
# push!(custombb_time, 0)
push!(custombb_time, geom_shifted_mean(df_AF_ind[ind_term_AF_ind_CustomBB,:timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_AF_corr[ind_term_AF_corr_Boscia,:timeBoscia]))
push!(scip_time, geom_shifted_mean(df_AF_corr[ind_term_AF_corr_Scip,:timeScip]))
push!(pajarito_time, geom_shifted_mean(df_AF_corr[ind_term_AF_corr_Pajarito,:timePajarito]))
# push!(custombb_time, 0)
push!(custombb_time, geom_shifted_mean(df_AF_corr[ind_term_AF_corr_CustomBB,:timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_A_ind[ind_term_A_ind_Boscia,:timeBoscia]))
push!(scip_time, 0)
push!(pajarito_time, geom_shifted_mean(df_A_ind[ind_term_A_ind_Pajarito,:timePajarito]))
push!(custombb_time, geom_shifted_mean(df_A_ind[ind_term_A_ind_CustomBB, :timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_A_corr[ind_term_A_corr_Boscia,:timeBoscia]))
push!(scip_time, 0)
push!(pajarito_time, geom_shifted_mean(df_A_corr[ind_term_A_corr_Pajarito,:timePajarito]))
push!(custombb_time, geom_shifted_mean(df_A_corr[ind_term_A_corr_CustomBB, :timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_DF_ind[ind_term_DF_ind_Boscia,:timeBoscia]))
push!(scip_time, geom_shifted_mean(df_DF_ind[ind_term_DF_ind_Scip,:timeScip]))
push!(pajarito_time, geom_shifted_mean(df_DF_ind[ind_term_DF_ind_Pajarito,:timePajarito]))
# push!(custombb_time, 0)
push!(custombb_time, geom_shifted_mean(df_DF_ind[ind_term_DF_ind_CustomBB,:timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_DF_corr[ind_term_DF_corr_Boscia,:timeBoscia]))
push!(scip_time, geom_shifted_mean(df_DF_corr[ind_term_DF_corr_Scip,:timeScip]))
push!(pajarito_time, geom_shifted_mean(df_DF_corr[ind_term_DF_corr_Pajarito,:timePajarito]))
# push!(custombb_time, 0)
push!(custombb_time, geom_shifted_mean(df_DF_corr[ind_term_DF_corr_CustomBB,:timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_D_ind[ind_term_D_ind_Boscia,:timeBoscia]))
push!(scip_time, 0)
push!(pajarito_time, geom_shifted_mean(df_D_ind[ind_term_D_ind_Pajarito,:timePajarito]))
push!(custombb_time, geom_shifted_mean(df_D_ind[ind_term_D_ind_CustomBB, :timeCustomBB]))
push!(boscia_time, geom_shifted_mean(df_D_corr[ind_term_D_ind_Boscia,:timeBoscia]))
push!(scip_time, 0)
push!(pajarito_time, geom_shifted_mean(df_D_corr[ind_term_D_ind_Pajarito,:timePajarito]))
push!(custombb_time, geom_shifted_mean(df_D_corr[ind_term_D_ind_CustomBB, :timeCustomBB]))
boscia_time = round.(boscia_time, digits=3)
pajarito_time = round.(pajarito_time, digits=3)
scip_time = round.(scip_time, digits=3)
custombb_time = round.(custombb_time, digits=3)
## number nodes and Cuts
println("Cuts and Nodes")
push!(boscia_nodes, custom_mean(df_AF_ind[!, :numberNodesBoscia]))
push!(scip_cuts, custom_mean(df_AF_ind[!,:numberCutsScip]))
push!(pajarito_cuts, custom_mean(df_AF_ind[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_AF_ind[!,:numberIterPajarito]))
# push!(custombb_nodes, 0)
push!(custombb_nodes, custom_mean(df_AF_ind[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_AF_corr[!, :numberNodesBoscia]))
push!(scip_cuts, custom_mean(df_AF_corr[!,:numberCutsScip]))
push!(pajarito_cuts, custom_mean(df_AF_corr[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_AF_corr[!,:numberIterPajarito]))
# push!(custombb_nodes, 0)
push!(custombb_nodes, custom_mean(df_AF_corr[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_A_ind[!, :numberNodesBoscia]))
push!(scip_cuts, 0)
push!(pajarito_cuts, custom_mean(df_A_ind[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_A_ind[!,:numberIterPajarito]))
push!(custombb_nodes, custom_mean(df_A_ind[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_A_corr[!, :numberNodesBoscia]))
push!(scip_cuts, 0)
push!(pajarito_cuts, custom_mean(df_A_corr[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_A_corr[!,:numberIterPajarito]))
push!(custombb_nodes, custom_mean(df_A_corr[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_DF_ind[!, :numberNodesBoscia]))
push!(scip_cuts, custom_mean(df_DF_ind[!,:numberCutsScip]))
push!(pajarito_cuts, custom_mean(df_DF_ind[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_DF_ind[!,:numberIterPajarito]))
# push!(custombb_nodes, 0)
push!(custombb_nodes, custom_mean(df_DF_ind[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_DF_corr[!, :numberNodesBoscia]))
push!(scip_cuts, custom_mean(df_DF_corr[!,:numberCutsScip]))
push!(pajarito_cuts, custom_mean(df_DF_corr[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_DF_corr[!,:numberIterPajarito]))
# push!(custombb_nodes, 0)
push!(custombb_nodes, custom_mean(df_DF_ind[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_D_ind[!, :numberNodesBoscia]))
push!(scip_cuts, 0)
push!(pajarito_cuts, custom_mean(df_D_ind[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_D_ind[!,:numberIterPajarito]))
push!(custombb_nodes, custom_mean(df_D_ind[!,:numberNodesCustomBB]))
push!(boscia_nodes, custom_mean(df_D_corr[!, :numberNodesBoscia]))
push!(scip_cuts, 0)
push!(pajarito_cuts, custom_mean(df_D_corr[!,:numberCutsPajarito]))
push!(pajarito_iters, custom_mean(df_D_corr[!,:numberIterPajarito]))
push!(custombb_nodes, custom_mean(df_D_corr[!,:numberNodesCustomBB]))
# rounding
boscia_nodes = convert.(Int64, round.(boscia_nodes))
scip_cuts = convert.(Int64, round.(scip_cuts))
pajarito_cuts = convert.(Int64, round.(pajarito_cuts))
pajarito_iters = convert.(Int64, round.(pajarito_iters))
custombb_nodes = convert.(Int64, round.(custombb_nodes))
## relative gap
println("Relative gap")
push!(boscia_rel_gap, geo_mean(df_AF_ind[!,:relDualGapBoscia]))
push!(scip_rel_gap, geo_mean(df_AF_ind[!,:relDualGapScip]))
push!(pajarito_rel_gap, geo_mean(df_AF_ind[!,:relDualGapPajarito]))
# push!(custombb_rel_gap, 0)
push!(custombb_rel_gap, geo_mean(df_AF_ind[!,:relDualGapCustomBB]))
push!(boscia_rel_gap, geo_mean(df_AF_corr[!,:relDualGapBoscia]))
push!(scip_rel_gap, geo_mean(df_AF_corr[!,:relDualGapScip]))
push!(pajarito_rel_gap, geo_mean(df_AF_corr[!,:relDualGapPajarito]))
# push!(custombb_rel_gap, 0)
push!(custombb_rel_gap, geo_mean(df_AF_corr[!,:relDualGapCustomBB]))
push!(boscia_rel_gap, geo_mean(df_A_ind[!,:relDualGapBoscia]))
push!(scip_rel_gap, 0)
push!(pajarito_rel_gap, geo_mean(df_A_ind[!,:relDualGapPajarito]))
push!(custombb_rel_gap, geo_mean(df_A_ind[!,:relDualGapCustomBB]))
push!(boscia_rel_gap, geo_mean(df_A_corr[!,:relDualGapBoscia]))
push!(scip_rel_gap, 0)
push!(pajarito_rel_gap, geo_mean(df_A_corr[!,:relDualGapPajarito]))
push!(custombb_rel_gap, geo_mean(df_A_corr[!,:relDualGapCustomBB]))
push!(boscia_rel_gap, geo_mean(df_DF_ind[!,:relDualGapBoscia]))
push!(scip_rel_gap, geo_mean(df_DF_ind[!,:relDualGapScip]))
push!(pajarito_rel_gap, geo_mean(df_DF_ind[!,:relDualGapPajarito]))
# push!(custombb_rel_gap, 0)
push!(custombb_rel_gap, geo_mean(df_DF_ind[!,:relDualGapCustomBB]))
push!(boscia_rel_gap, geo_mean(df_DF_corr[!,:relDualGapBoscia]))
push!(scip_rel_gap, geo_mean(df_DF_corr[!,:relDualGapScip]))
push!(pajarito_rel_gap, geo_mean(df_DF_corr[!,:relDualGapPajarito]))
# push!(custombb_rel_gap, 0)