filename = "./PF/" + problem.name + "(" + str(len(problem.objectives)) + ")-PF.txt"

f = open(filename, "r")

true_PF = []

for line in f:

temp = []

for x in line.split():

temp.append(float(x))

true_PF.append(temp)

def sum(ll):

l = ll.fitness.fitness

# print "l: ", l

assert len(problem.objectives) == len(l), "Something is wrong here"

new = []

for i, o in enumerate(problem.objectives):

if o.lismore is False:

new.append(l[i])

else:

new.append(100 - l[i])

return min(new)

print "Length of frontier: ", len(lst)

chosen = lst[0]

for element in lst:

if sum(chosen) < sum(element):

chosen = element

def __init__(self,tests):

self.tests = tests

def doit(self,tagnote=""):

def __init__(self,tests):

self.tests = tests

def doit(self,tagnote=""):

def __init__(self,tests, Configurations):

self.tests = tests

self.Configurations = Configurations

def doit(self,tagnote=""):

for problem in self.tests.problems:

def __init__(self, tag="stats", tests = None):

self.filename = DEFECT_PREDICT_PREFIX + "DefectPredict.xml"

self.tag = tag

self.tests = tests

def doit(self, tagnote=""):

if self.tag == "stats":

self.doStatistics()

elif self.tag == "Charts":

self.doCharts()

elif self.tag == "ranking":

self.doRanks()

def doStatistics(self):

parseXML(self.filename,self.tag)

def doCharts(self):

parseXML(self.filename,self.tag)

def doRanks(self):

assert(self.tests != None), "Problems not passed"

print self.filename

def __init__(self, problems, algorithms):

self.problems = problems

self.algorithms = algorithms

def __str__(self):

def __init__(self,tests,reports, configurations):

self.tests = tests

self.reports = reports

self.configurations = configurations

def doTests(self):

sc2 = open(DATA_PREFIX + SUMMARY_RESULTS + DATA_SUFFIX, 'w')

# Main control loop

representatives = [] # List of resulting final generations (stat boxe datatype)

for problem in self.tests.problems:

print "# " * 30

for algorithm in self.tests.algorithms:

# Initialize Data file for recording summary information [for just this problem + algorithm]

backend = problem.name + "_" + algorithm.name + ".txt"

# Decision Data

# filename = problem.name + "-p" + str(self.configurations["Universal"]["Population_Size"]) + "-d" + str(len(problem.decisions)) + "-o" + str(len(problem.objectives))+"_"+algorithm.name+DATA_SUFFIX

filename = problem.name + "-p" + str(100) + "-d" + str(len(problem.decisions)) + "-o" + str(len(problem.objectives))+"_"+algorithm.name+DATA_SUFFIX

dbt = open(DATA_PREFIX + DECISION_BIN_TABLE + "_" + filename, 'w')

sr = open(DATA_PREFIX + SUMMARY_RESULTS + filename, 'w')

rrs = open(DATA_PREFIX + RRS_TABLE + "_" + filename, 'w')

# Results Record:

# # # Every generation

# # # Decisions + Objectives

# Summary Record

# - Best Generation Only

# - Number of Evaluations + Aggregated Objective Score

# -

fa = open("Data/results_"+filename, 'w')

strings = ["NumEval"] \

+ [obj.name + "_median,(%chg),"

+ obj.name + "_spread" for obj in problem.objectives]\

+ ["IBD,(%chg), IBS"] + ["IGD,(%chg)"]

for s in strings: fa.write(s + ",")

fa.write("\n")

fa.close()

print "+ " * 30

# Repeat Core

for repeat in range(self.configurations["Universal"]["Repeats"]):

print "#<-------|" + problem.name + " + " + algorithm.name + " + " + str(repeat) + "|------->#"

# Run

start = time.time()

statBox = jmoo_evo(problem, algorithm, self.configurations)

end = time.time()

# Find best generation

representative = statBox.box[0]

for r,rep in enumerate(statBox.box):

# for indi in rep.population:

# print indi

if rep.IBD < representative.IBD:

representative = statBox.box[r]

representatives.append(representative)

# Decision Bin Data

s = ""

for row in representative.population:

for dec in row.decisionValues:

s += str("%10.2f" % dec) + ","

if row.valid:

for obj in row.fitness.fitness:

s += str("%10.2f" % obj) + ","

else:

for obj in problem.objectives:

s += "?" + ","

s += str(representative.numEval) + ","

s += "\n"

dbt.write(s)

baseline = problem.referencePoint

s = ""

for row in representative.population:

#if not row.valid:

# row.evaluate()

if row.valid:

for o,base,obj in zip(row.fitness.fitness, baseline, problem.objectives ):

c = percentChange(o, base, obj.lismore, obj.low, obj.up)

s += c + ","

s += str(representative.numEval) + ","

for o,base,obj in zip(row.fitness.fitness, baseline, problem.objectives ):

c = str("%12.2f" % o)

s += c + ","

s += "\n"

rrs.write(s)

#output every generation

for box in [representative]:

s_out = ""

s_out += str(self.configurations["Universal"]["Population_Size"]) + ","

s_out += problem.name + "-p" + str(self.configurations["Universal"]["Population_Size"]) + "-d" + str(len(problem.decisions)) + "-o" + str(len(problem.objectives)) + ","

s_out += algorithm.name + ","

s_out += str(box.numEval) + ","

for low in representative.fitnessMedians:

s_out += str("%10.2f" % low) + ","

s_out += str("%10.2f" % box.IBD) + "," + str("%10.2f" % box.IBS) + "," + str((end-start))

sr.write(s_out + "\n")

sc2.write(s_out + "\n")

# Finish

print " # Finished: Celebrate! # " #+ " Time taken: " + str("%10.5f" % (end-start)) + " seconds."

def remove_dominated_solution(self, problem, final_generation):

# change format from jmoo_individula to deap_structure

deap_final_generation = deap_format(problem, final_generation)

front = ParetoFront()

front.update(deap_final_generation)

# Copy from DEAP structure to JMOO structure

population = []

for i,dIndividual in enumerate(front):

cells = []

for j in range(len(dIndividual)):

cells.append(dIndividual[j])

population.append(jmoo_individual(problem, cells, dIndividual.fitness.values))

return population

def medianlist(self, list):

print "List: ", list

ret = []

for i in xrange(len(list[0])):

temp = []

print "i: ", i

for l in list:

temp.append(l[i])

ret.append(median(temp))

return ret

def doReports(self,thing=""):

for report in self.reports: