def get_hyper_volume(problem, reference_point, results, Configurations):
"""Receives list of lists"""
# +The results should only be the non dominated points
# non dominated sorting
from jmoo_algorithms import deap_format
dIndividuals = deap_format(problem, results)
# get only the first front
from Algorithms.DEAP.tools.emo import sortNondominated
first_front = sortNondominated(dIndividuals, len(dIndividuals), first_front_only=True)
from itertools import chain
chosen = list(chain(*first_front))
# Copy from DEAP structure to JMOO structure
from jmoo_individual import jmoo_individual
population = []
for i, dIndividual in enumerate(chosen):
cells = []
for j in range(len(dIndividual)):
cells.append(dIndividual[j])
population.append(jmoo_individual(problem, cells, [f for f in dIndividual.fitness.values]))
# +The results should only be the non dominated points
# Find the min and max of the objectives
MU = Configurations["Universal"]["Population_Size"]
# Normalization
filename = "Data/" + problem.name + "-p" + str(MU) + "-d" + str(len(problem.decisions)) + "-o" + \
str(len(problem.objectives)) + "-dataset.txt"
import csv
input = open(filename, 'rb')
reader = csv.reader(input, delimiter=',')
#Use the csv file to build the initial population
for k,p in enumerate(reader):
if k > MU:
problem.objectives[k-MU-1].med = float(p[1])
low_not_found = False
up_not_found = False
if problem.objectives[k-MU-1].low is None:
problem.objectives[k-MU-1].low = float(p[0])
low_not_found = True
if problem.objectives[k-MU-1].up is None:
problem.objectives[k-MU-1].up = float(p[2])
up_not_found = True
rangeX5 = (problem.objectives[k-MU-1].up - problem.objectives[k-MU-1].low)*5
if low_not_found: problem.objectives[k-MU-1].low -= rangeX5
if up_not_found: problem.objectives[k-MU-1].up += rangeX5
# convert the objectives as list of list
results = [[(f-problem.objectives[i].low)/(problem.objectives[i].up - problem.objectives[i].low)
for i, f in enumerate(pop.fitness.fitness)] for pop in population]
normalized_reference_point = [1 for _ in reference_point]
HV = HyperVolume(normalized_reference_point)
return HV.compute(results)
population.append(
jmoo_individual(problem, line[:decision], line[decision:]))
return population
def convert_jmoo(pareto_fronts, front_no_d=0):
tpopulation = []
for front_no, front in enumerate(pareto_fronts):
for i, dIndividual in enumerate(front):
cells = []
for j in xrange(len(dIndividual)):
cells.append(dIndividual[j])
tpopulation.append(
jmoo_individual(problem, cells, dIndividual.fitness.values))
tpopulation[-1].front_no = front_no_d
return tpopulation
population = read_file()
os.chdir(
"../../.."
) # Since the this file is nested so the working directory has to be changed
from Algorithms.DEAP.tools.emo import sortNondominated
Individuals = jmoo_algorithms.deap_format(problem, population)
pareto_fronts = sortNondominated(Individuals, jmoo_properties.MU)
population = convert_jmoo(pareto_fronts[:-1])
last_front = convert_jmoo(pareto_fronts[-1:])
print "Length of population: ", len(population)
print "Front: ", len(last_front)
print "Remain: ", jmoo_properties.MU - len(population)
for line in open("test_non_dominated_sorting.txt", "r").readlines():
decision = 7
objectives = 3
line = [float(a) for a in line.split()[1:]]
population.append(jmoo_individual(problem, line[:decision], line[decision:]))
return population
def convert_jmoo(pareto_fronts, front_no_d=0):
tpopulation = []
for front_no, front in enumerate(pareto_fronts):
for i, dIndividual in enumerate(front):
cells = []
for j in xrange(len(dIndividual)):
cells.append(dIndividual[j])
tpopulation.append(jmoo_individual(problem, cells, dIndividual.fitness.values))
tpopulation[-1].front_no = front_no_d
return tpopulation
population = read_file()
os.chdir("../../..") # Since the this file is nested so the working directory has to be changed
from Algorithms.DEAP.tools.emo import sortNondominated
Individuals = jmoo_algorithms.deap_format(problem, population)
pareto_fronts = sortNondominated(Individuals, jmoo_properties.MU)
population = convert_jmoo(pareto_fronts[:-1])
last_front = convert_jmoo(pareto_fronts[-1:])
print "Length of population: ", len(population)
print "Front: ", len(last_front)
print "Remain: ", jmoo_properties.MU - len(population)
