def __test__():
# global Testobj
sys.setrecursionlimit(30)
file = 'pooltest5.pkl'
filecls = 'pooltest_cls.pkl'
cls = Testobj0
print(cls.param)
if os.path.isfile(file):
objs = ut.load(file)
cls = ut.load(filecls)
print(cls.param)
else:
pool = []
for i in range(1000):
obj = cls(pool)
objs = [x for x in pool]
ut.save(file, objs)
ut.save(filecls, cls)
print(len(objs))
obj = objs[500]
# print(pool[500], len(pool))
print(obj.id, obj.data)
obj()
print(cls.param)
def main(model, out):
n_dim = 10
popsize = 30
epoch = 100 * 5
problem = Problem()
with Environment() as env:
indiv_pool = env.register(Individual)
indiv_pool.cls.set_weight([1, -1])
initializer = UniformInitializer(n_dim)
creator = Creator(initializer, indiv_pool)
crossover = SimulatedBinaryCrossover(rate=0.9, eta=100)
if model == 'moead':
ksize = 10
options = {
"ksize": ksize,
"normalization": True,
"crossover": crossover
}
optimizer = MOEAD(problem=problem, pool=indiv_pool, **options)
optimizer.weight_generator(nobj=4, divisions=50)
popsize = int(popsize)
epoch = epoch
elif model == 'nsga2':
optimizer = NSGA2(problem=problem,
pool=indiv_pool,
normalization=False)
elif model == 'para':
optimizer = NSGA2_para(problem=problem, pool=indiv_pool)
else:
raise Exception('Unexpected model name')
population = optimizer.init_population(creator, popsize=popsize)
history = [population]
print("obj weight:", population[0].data.weight)
for i in range(1, epoch + 1):
if i % 50 == 0:
print("epoch ", i)
population = optimizer(population)
history.append(population)
if i == epoch:
file = f'popsize{popsize}_epoch{epoch}_{ut.strnow("%Y%m%d_%H%M%S")}.pkl'
file = os.path.join(out, file)
if not os.path.exists(out):
os.makedirs(out)
print('save:', file)
ut.save(file, (env, optimizer, history))
return env, optimizer, history
def local_main():
n_dim = 30
popsize = 100
problem = zdt1
with Environment() as env:
# 個体クラス
indiv_type = Individual
# 初期個体生成クラス
indiv_pool = env.register(indiv_type)
# 遺伝子生成クラス
initializer = UniformInitializer(n_dim)
creator = Creator(initializer, indiv_pool)
# # 適応度クラス
# fit_type = Fitness
# # 初期個体生成クラス
# evaluator = env.register(fit_type)
###
# optimizer = NSGA2(problem=problem, pool=indiv_pool)
optimizer = MOEAD(problem=problem, pool=indiv_pool, ksize=5)
# optimizer.set_initializer(Initializer(3))
# optimizer.setup(problem)
### Additional setting ###
# optimizer.initializer = initializer
# optimizer.n_cycle = None
# optimizer.alternation = 'replace'
##########################
# indivs = [creator() for _ in range(popsize)]
# population = Population([ind.evaluate(problem) for ind in indivs])
# optimizer.calc_fitness(population)
population = optimizer.init_population(creator, popsize=popsize)
history = [population]
# print(population[0].data.origin)
# return
for i in range(100):
population = optimizer(population)
plot(population)
origin = population[0].data.origin.origin or []
# print([x.id for x in origin], '->', population[0].data.id)
history.append(population)
if i % 50 == 50 - 1:
ut.save(f'result/temp{i}.pkl', history)
plt.show()
return env, optimizer, history
def main1(out='result', force=False, clear_directory=False):
''' 最適化実行(ミニマル)
'''
''' ======== 最適化パラメータの定義 ======== '''
problem = zdt1
n_dim = 30
popsize = 100
epoch = 200
with Environment() as env:
''' ======== 必要なオブジェクトの準備 ======== '''
# 個体管理クラス
indiv_pool = env.register(Individual)
# 遺伝子生成クラス
initializer = UniformInitializer(n_dim)
# 初期個体生成クラス
creator = Creator(initializer, indiv_pool)
# 最適化オペレータ
op0 = NSGA2(problem=problem, pool=indiv_pool)
op1 = MOEAD(problem=problem, pool=indiv_pool, ksize=5)
optimizer = op0
''' ======== 初期集団の作成 ======== '''
population = optimizer.init_population(creator, popsize=popsize)
history = [population]
''' ======== 最適化 ======== '''
for i in range(epoch):
print('epoch', i + 1)
population = optimizer(population)
history.append(population)
if i % 50 == 50-1:
ut.save(f'result/temp{i}.pkl', history)
if i >= epoch // 2:
optimizer = op1
''' ======== 結果保存 ======== '''
data = env, optimizer, history
ut.save(f'result/optimize_#{ut.snow}.pkl', data)
return env, optimizer, history
def main1(out='result', force=False, clear_directory=False):
''' GAテスト & プロット
'''
###
def ln(ind, origin):
if not origin:
return []
return list(zip(origin[0].value, ind.value, origin[1].value))
def dist(ind, origin):
if not origin:
return 0
values = np.array([[ind.value, par.value] for par in origin])
diff = values[:, :, 0] - values[:, :, 1]
dists = np.sqrt(np.sum(diff**2, axis=1))
return np.sum(dists)
fig, ax = plt.subplots()
def plot(pop):
pairs = [(fit.data, fit.data.origin.origin or ()) for fit in pop]
parents = list(chain(*(x[1] for x in pairs)))
lines = [ln(ind, origin) for ind, origin in pairs]
# print(lines)
# exit()
if lines and [x for x in lines if x]:
dists = [dist(ind, origin) for ind, origin in pairs]
print(sum(dists))
ax.cla()
cm = plt.get_cmap('jet')
# print(cm(10))
# exit()
if parents:
x_p, y_p = np.array([ind.value for ind in parents]).T
ax.scatter(x_p, y_p, c='r')
for i, l in enumerate(lines):
if l:
plt.plot(*l, c=cm(i / (len(lines) + 1)), linewidth=0.5)
x, y = np.array([fit.data.value for fit in pop]).T
ax.scatter(x, y, c='b')
plt.pause(1e-10)
###
def local_main():
n_dim = 30
popsize = 100
problem = zdt1
with Environment() as env:
# 個体クラス
indiv_type = Individual
# 初期個体生成クラス
indiv_pool = env.register(indiv_type)
# 遺伝子生成クラス
initializer = UniformInitializer(n_dim)
creator = Creator(initializer, indiv_pool)
# # 適応度クラス
# fit_type = Fitness
# # 初期個体生成クラス
# evaluator = env.register(fit_type)
###
# optimizer = NSGA2(problem=problem, pool=indiv_pool)
optimizer = MOEAD(problem=problem, pool=indiv_pool, ksize=5)
# optimizer.set_initializer(Initializer(3))
# optimizer.setup(problem)
### Additional setting ###
# optimizer.initializer = initializer
# optimizer.n_cycle = None
# optimizer.alternation = 'replace'
##########################
# indivs = [creator() for _ in range(popsize)]
# population = Population([ind.evaluate(problem) for ind in indivs])
# optimizer.calc_fitness(population)
population = optimizer.init_population(creator, popsize=popsize)
history = [population]
# print(population[0].data.origin)
# return
for i in range(100):
population = optimizer(population)
plot(population)
origin = population[0].data.origin.origin or []
# print([x.id for x in origin], '->', population[0].data.id)
history.append(population)
if i % 50 == 50 - 1:
ut.save(f'result/temp{i}.pkl', history)
plt.show()
return env, optimizer, history
###
def resume_main(env, optimizer, history):
print('resume_main')
for i, population in enumerate(history):
plot(population)
origin = population[0].data.origin.origin or []
# print([x.id for x in origin], '->', population[0].data.id)
plt.show()
file = 'test_moead.pkl'
if os.path.exists(file) and not force:
env, optimizer, history = ut.load(file)
resume_main(env, optimizer, history)
else:
env, optimizer, history = local_main()
ut.save(file, (env, optimizer, history))
def __test__():
obj = TestClass('abc')
ut.save('test.pkl', obj)
obj = ut.load('test.pkl')