def get_first_pareto(pop):
'''
get only first pareto
:param pop:
:return:
'''
return GA.get_pareto(pop)[0]
def run_NSGA3(input_fname):
modify = modifier.modify_testcase(input_fname)
dim = modify.get_datasize()
reference = get_ref()
population = GA.initial_genes(dim,GLOB.POP)
population_history = [population]
for i in range(GLOB.MAX_IT):
new_pop = GA.crossover(population)
GA.evaluate(new_pop,modify)
pareto = GA.get_pareto(new_pop)
new_pop = select(pareto)
population = new_pop
population_history.append(population)
first_pareto = get_first_pareto(population) #final result
def read_file(SIR_name, version):
'''
Read files in Result directory and get data
return data by each MOEA and trial
return reference points for the specific SIR program and version
data: dictionary, key = MOEA value = 2D array.
first dimension for each trial
second dimension for data
ex) dict['NSGA2'] = [[pt1,pt2, ...], [pt100,pt101, ...], ...]
each pt = [evaluation metric1, metric2, metric3, ...]
ref_pt: array.
ex) ref_pt = [pt1, pt2, pt3, ...]
each pt = [evaluation metric1, metric2, metric3, ...]
:param SIR_name: String, SIR program name
:param version: int, version of SIR program
:return:
'''
directory = GLOB.RESULT_DIRECTORY + SIR_name + '/'
data = dict()
ref_pt = []
for MOEA in GLOB.TRY_ALGORITHM:
data_moea = [[] for i in range(GLOB.TRIALS_PER_VERSION)]
for i in range(GLOB.TRIALS_PER_VERSION):
fname = 'eval_' + SIR_name + '_version' + str(version) + '_' + MOEA + '_trial' + str(i) + '.csv'
with open(directory + fname, 'r') as f:
f.readline()
rdr = csv.reader(f)
for line in rdr:
for j in range(len(line)):
line[j] = float(line[j])
data_moea[i].append(np.array(line))
gene = GA.Gene_Info(None)
gene.set_eval(np.array(line))
ref_pt.append(gene)
ref_pt = GA.get_pareto(ref_pt)[0] #update reference point
data[MOEA] = data_moea
for i in range(len(ref_pt)):
ref_pt[i] = ref_pt[i].get_eval()
return data, ref_pt
def run_NSGA2(SIR_name, version, test_size):
evaluator = evaluation.VEval(SIR_name, version, test_size)
dim = test_size
population = GA.initial_genes(dim, GLOB.POP, version)
if GLOB.DEBUG:
print('0', len(population))
population_history = [population]
for i in range(GLOB.MAX_IT):
new_pop = GA.crossover(population, mr=1 / dim)
GA.evaluate(new_pop, evaluator)
pareto = GA.get_pareto(new_pop)
new_pop = select(pareto)
population = new_pop
population_history.append(population)
if GLOB.DEBUG:
print(i, len(population))
first_pareto = get_first_pareto(population) #final result
return first_pareto