def getbininfo(nop, folders, methods):
writer = pandas.ExcelWriter(folders[nop] + 'IndividualBinInfo.xlsx',
engine='openpyxl')
for opal in range(nop):
itemfile = glob(folders[opal] + '*.txt')
binc, binh, items = itemmaker.makeitems(itemfile[0])
info = itemmaker.getiteminfo(items)
xs, ys = getxys(folders[opal], len(items))
for f in range(len(xs)):
xmatrix = np.zeros((len(items), len(items)))
for i in range(len(items)):
for j in range(len(items)):
xmatrix[i, j] = xs[f].get_value(i, j, takeable=True)
binws = np.dot(xmatrix, info[0])
binhs = np.dot(xmatrix, info[1])
binws_series = []
binhs_series = []
end = np.where(ys[f] == 0)
for i in range(end[0][0]):
binws_series.append(binws[i, 0])
binhs_series.append(binhs[i, 0])
if f == 0:
dfw = pandas.DataFrame(binws_series)
dfh = pandas.DataFrame(binhs_series)
else:
df1 = pandas.DataFrame(binws_series)
df2 = pandas.DataFrame(binhs_series)
dfw = pandas.concat([dfw, df1], ignore_index=True, axis=1)
dfh = pandas.concat([dfh, df2], ignore_index=True, axis=1)
dfw.to_excel(writer, sheet_name=methods[opal] + '_weight')
dfh.to_excel(writer, sheet_name=methods[opal] + '_height')
writer.save()
def main():
# Get info to create items
n = eval(input('Please enter the number of items to be sorted: \n'))
folder = input(
'Please enter the name of the folder where your input file is: \n')
datafile = input('Please enter the name of the input file: \n')
# Create item objects and initialize a bin packing problem class
data = folder + datafile
binc, binh, items = makeitems(data)
bpp = BPP(n, binc, binh, items)
# Input items into the heuristic in the way they were ordered in
# the datafile. Each "gene" should correspond to an item's index.
solid = 1 # give this solution an id number
chromosome = list(range(1, n + 1))
x, y = ed(solid, chromosome, bpp)
np.savetxt(folder + str(solid) + '_x.txt',
x,
fmt='%i',
header='Item Location Matrix x:')
np.savetxt(folder + str(solid) + '_y.txt',
y,
fmt='%i',
header='Bins Used Matrix y:')
def setUp(self):
data = 'GAMMA-PC/SBSBPP500/Experiment01/SBSBPP500_run1.txt'
self.n = 500
pop = 50 # members per gen.
end = 750 # function evaluations
binc, binh, items = makeitems(data)
moop = mop.MOproblem(pop, items)
self.bpp = grasp.BPP(self.n, binc, binh, items, moop)
def setUp(self):
data = 'example/example500.txt'
self.n = 500
pop = 50 # members per gen.
end = 750 # function evaluations
binc, binh, items = makeitems(data)
moop = mop.MOproblem(pop, items)
self.bpp = grasp.BPP(self.n, binc, binh, items, moop)
def moma(n, folder, datafile):
existing_files = glob(folder + '*.out')
filename = folder + 'run%d.out' % (len(existing_files) + 1)
data = folder + datafile
# Initialize algorithm
pop = 100 # members per gen.
end = 25000 # function evaluations or 250 gen.
outf.startout(filename, n, end, data, 'MOMA')
startt = datetime.now()
print(' ', startt)
print(
'*******************************************************************************'
)
print(' Method: MOMA\n')
print(' data: ', datafile)
print(
'*******************************************************************************'
)
binc, binh, items = makeitems(data)
bpp = bp.BPP(n, binc, binh, items)
moop = mop.MOproblem(pop, items)
gen = Generation(n, pop, end, items, bpp, moop)
gen.initialp()
gen.makeq()
# NSGA-II - Local search
while not gen.endgen():
gen.rungen()
outf.genout(filename, gen.gett(), pop, gen.getq(), [gen.getarch(), []])
# Get final nondominated set
aslimit = 75
r, allfronts = fnds(gen.getarch())
if len(allfronts[0]) > aslimit:
gen.fittruncation(allfronts[0], aslimit)
ndset = approx(gen.getarch())
# Make output
see(ndset, folder)
import csv
with open(folder + 'ApproximationSet.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile, dialect='excel', delimiter=',')
csvwriter.writerow(['Solution ID', 'f[1]', 'f[2]', 'f[3]'])
for m in ndset:
csvwriter.writerow(
[m.getindex(),
m.getbins(),
m.getmaxh(),
m.getavgw()])
outf.endout(filename)
print('This algorithm has completed successfully.')
def algorithm(n, folder, datafile):
existing_files = glob(folder + '*.out')
fname = folder + 'run%d.out' % (len(existing_files) + 1)
data = folder + datafile
# Initialize algorithm
pop = 100 # members per gen.
end = 25000 # function evaluations
outf.startout(fname, n, end, data, 'GAMMA-PC')
startt = datetime.now()
print(' ', startt)
print(
'*******************************************************************************'
)
print(' Method: GAMMA-PC\n')
print(' data: ', datafile)
print(
'*******************************************************************************'
)
binc, binh, items = makeitems(data)
moop = mop.MOproblem(pop, items)
bpp = grasp.BPP(n, binc, binh, items, moop)
gen = Generation(n, pop, end, items, bpp, moop)
gen.initialq()
# Remove this after finishing
import warnings
warnings.simplefilter('error', RuntimeWarning)
# Algorithm
while not gen.endgen():
outf.gen_probabilities(fname,
gen.gett() + 1, **gen.get_probabilities())
gen.rungen()
outf.genout(fname,
gen.gett(),
pop,
gen.getq(),
gen.getarch(),
onlyapprox=True)
# Make output
ndset = gen.finalapproxset()
savefitvals(ndset, folder)
savexys(ndset, folder)
see(ndset, folder)
outf.endout(fname)
print('This algorithm has completed successfully.')
def nsgaii(n, folder, file):
import outformat as outf
from glob import glob
existing_files = glob(folder + '*.out')
filename = folder + 'run%d.out' % (len(existing_files) + 1)
data = folder + file
# Initialize algorithm
pop = 100 # members per gen.
end = 25000 # function evaluations or 250 gen.
outf.startout(filename, n, end, data, 'NSGA-II')
startt = datetime.datetime.now()
print(' ', startt)
print(
'*******************************************************************************'
)
print(' Method: NSGA-II\n')
print(' data: ', file)
print(
'*******************************************************************************'
)
binc, binh, items = makeitems(data)
bpp = bp.BPP(n, binc, binh, items)
gen = Generation(n, pop, end, items, bpp)
gen.initialp()
gen.makeq()
# NSGA-II
while not gen.endgen():
gen.rungen()
outf.genout(filename, gen.gett(), pop, gen.getq(), gen.getfnts())
# Make output
ndset = approx(gen.getarch())
see(ndset, folder)
import csv
with open(folder + 'ApproximationSet.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile, dialect='excel', delimiter=',')
csvwriter.writerow(['Solution ID', 'f[1]', 'f[2]', 'f[3]'])
for m in ndset:
csvwriter.writerow(
[m.getindex(),
m.getbins(),
m.getmaxh(),
m.getavgw()])
outf.endout(filename)
print('This algorithm has completed successfully.')
def main():
# Get info to create items
n = eval(input('Please enter the number of items to be sorted: \n'))
folder = input('Please enter the name of the folder where your input file is: \n')
datafile = input('Please enter the name of the input file: \n')
# Create item objects and initialize a bin packing problem class
data = folder + datafile
binc, binh, items = makeitems(data)
bpp = BPP(n, binc, binh, items)
# Input items into the heuristic in the way they were ordered in
# the datafile. Each "gene" should correspond to an item's index.
solid = 1 # give this solution an id number
chromosome = list(range(1, n + 1))
x, y = ed(solid, chromosome, bpp)
np.savetxt(folder + str(solid) + '_x.txt', x, fmt='%i', header='Item Location Matrix x:')
np.savetxt(folder + str(solid) + '_y.txt', y, fmt='%i', header='Bins Used Matrix y:')
def recheck(n, folder, filename, flag=None):
# This module allows the user to recheck solutions for constraint
# violations after calculations have been completed.
# - n is the number of items to be sorted
# - folder is the main folder where the input file is located
# - filename is the input file name
# - flag (optional) is the name of the folder where the solution
# matrices are stored
data = folder + filename
# Reformulate item objects and bin packing problem
binc, binh, items = itemmaker.makeitems(data)
bpp = bp.BPP(n, binc, binh, items)
print('Max bin weight:', binc)
print('Max bin height:', binh)
# Go through each solution (x-y pair) to find violations
xs, ys, solids = getxys(folder + flag, n)
for m in range(len(xs)):
concheck(solids[m], xs[m], bpp)
xycheck(solids[m], xs[m], ys[m])
def recheck(n, folder, filename, flag=None):
# This module allows the user to recheck solutions for constraint
# violations after calculations have been completed.
# - n is the number of items to be sorted
# - folder is the main folder where the input file is located
# - filename is the input file name
# - flag (optional) is the name of the folder where the solution
# matrices are stored
data = folder + filename
# Reformulate item objects and bin packing problem
binc, binh, items = itemmaker.makeitems(data)
bpp = bp.BPP(n, binc, binh, items)
print('Max bin weight:', binc)
print('Max bin height:', binh)
# Go through each solution (x-y pair) to find violations
xs, ys, solids = getxys(folder + flag, n)
for m in range(len(xs)):
concheck(solids[m], xs[m], bpp)
xycheck(solids[m], xs[m], ys[m])
def getbinplots(nop, folders, methods):
# This function sorts through the data to make plots about
# individual bin characteristics.
print('Making Bin Weights plot.')
wplot = pyplot.figure()
ax = wplot.add_subplot(111)
dfseries = []
for opal in range(nop):
itemfile = glob(folders[opal] + '*.txt')
binc, binh, items = itemmaker.makeitems(itemfile[0])
info = itemmaker.getiteminfo(items)
xs, ys = getxys(folders[opal], len(items))
xplot = []
for f in range(len(xs)):
xplot.append(np.zeros((len(items), len(items))))
for i in range(len(items)):
for j in range(len(items)):
xplot[f][i, j] = xs[f].get_value(i, j, takeable=True)
sols_wavg, sols_wmax = binwsplot(xplot, ys, info[0], colors[opal],
methods[opal])
df1 = pandas.DataFrame({
'Solution Number': range(1, 1 + len(sols_wavg)),
'Weight': sols_wavg,
'Statistic': 'Average',
'Method': methods[opal]
})
df2 = pandas.DataFrame({
'Solution Number': range(1, 1 + len(sols_wavg)),
'Weight': sols_wmax,
'Statistic': 'Maximum',
'Method': methods[opal]
})
dfopal = pandas.concat((df1, df2), axis=0)
dfseries.append(dfopal)
fig = folders[nop] + 'BinWeight_comparison.eps'
ax.set_xlim(xmin=0)
ax.set_xlabel('Bin Weight')
ax.legend(loc='upper right', frameon=True)
pyplot.savefig(fig, format='eps', dpi=2000)
pyplot.close()
dfstats = pandas.concat(dfseries, axis=0)
getbinstatsplot(dfstats, folders[nop])
def momad(n, folder, file):
existing_files = glob(folder + '*.out')
filename = folder + 'run%d.out' % (len(existing_files) + 1)
data = folder + file
# Initialize Algorithm
end = 25000 # function evaluations
method = 'MOMAD'
outf.startout(filename, n, end, data, method)
startt = datetime.now()
print(' ', startt)
print('*******************************************************************************')
print(' Method: MOMAD \n')
print(' data: ', file)
print('*******************************************************************************')
if n <= 600:
nso = n
else:
nso = 600 # number of single-objective functions
binc, binh, items = makeitems(data)
bpp = bp.BPP(n, binc, binh, items)
moop = MOproblem(nso, items)
gen = MOMADgen(n, bpp, moop, items, end)
gen.initialize()
# MOMAD
while not gen.endgen():
gen.rungen()
fronts = []
fronts.append(gen.getpe())
fronts.append([])
outf.genout(filename, gen.gett(), nso, gen.getpl(), fronts)
see(gen.pe, folder)
import csv
with open(folder + 'ApproximationSet.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile, dialect='excel', delimiter=',')
csvwriter.writerow(['Solution ID', 'f[1]', 'f[2]', 'f[3]'])
for m in gen.pe:
csvwriter.writerow([m.getindex(), m.getbins(), m.getmaxh(), m.getavgw()])
outf.endout(filename)
print('This algorithm has completed successfully.')
def moepso(n, folder, inputfile):
existing_files = glob(folder + '*.out')
filename = folder + 'run%d.out' % (len(existing_files) + 1)
data = folder + inputfile
# Initialize Algorithm
pop = 500 # swarm members
end = 25000 # function evaluations
method = 'MOEPSO'
outf.startout(filename, n, end, data, method)
startt = datetime.now()
print(' ', startt)
print('*******************************************************************************')
print(' Method: MOEPSO \n')
print(' data: ', inputfile)
print('*******************************************************************************')
binc, binh, items = makeitems(data)
bpp = bp.BPP(n, binc, binh, items)
moop = mop.MOproblem(pop, items)
gen = MOEPSOgen(n, pop, end, bpp, moop, items)
# MOEPSO generations
while not gen.endgen():
gen.rungen()
fronts = [gen.getgbest(), []]
outf.genout(filename, gen.gett(), pop, gen.getp(), fronts)
see(gen.gbest, folder, 'variables/')
import csv
with open(folder + 'ApproximationSet.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile, dialect='excel', delimiter=',')
csvwriter.writerow(['Solution ID', 'f[1]', 'f[2]', 'f[3]'])
for m in gen.gbest:
csvwriter.writerow([m.getindex(), m.getbins(), m.getmaxh(), m.getavgw()])
outf.endout(filename)
print('This algorithm has completed successfully.')
