def ga_main1(out='result'):
file = ut.fsort(glob.glob(os.path.join(out, f'epoch*.pickle')))[-1]
optimizer = NSGA2.load(file=file)
elite = optimizer.get_elite()
print('elite:', len(elite))
for epoch, population in enumerate(optimizer):
print(epoch, end='\n')
plt.cla()
print([fit.rank for fit in population])
exit()
for i in range(1):
front = [x for x in population if x.rank == i]
if not front:
continue
x, y = np.array([x.data.value for x in front]).T
plt.scatter(x, y, label=f'{front[0][0]:.3f}')
plt.legend()
if epoch < len(optimizer) - 1:
plt.pause(0.2)
else:
plt.show()
def ga_result1(out='result'):
file = ut.fsort(glob.glob(os.path.join(out, f'epoch*.pickle')))[-1]
optimizer = NSGA2.load(file=file)
print('epoch:', len(optimizer))
elite = optimizer.get_elite()
print('elite:', len(elite))
for epoch in range(0, len(optimizer) - 1, 10):
print(epoch, end='\n')
# plt.cla()
population = optimizer[epoch]
population = optimizer.calc_rank(population)
for i in range(1, 2):
front = [x for x in population if x.rank == i]
# print(sorted([x.rank for x in population]))
if not front:
continue
x, y = np.array([x.data.value for x in front]).T
# print(front)
# return
plt.scatter(x, y, label=f'{front[0][0]:.3f}')
# plt.legend()
plt.xlim((0, 50))
# plt.ylim((0, 50))
if epoch < (len(optimizer) - 2) // 10 * 10:
plt.pause(0.2)
else:
plt.show()
def ga_main1(out='result'):
file = ut.fsort(glob.glob(os.path.join(out, f'epoch*.pickle')))[-1]
optimizer = MOEAD.load(file=file)
elite = optimizer.get_elite()
print('elite:', len(elite))
imax = len(optimizer)
for epoch, population in enumerate(optimizer):
print(epoch, end='\n')
plt.cla()
# print([fit.rank for fit in population])
# exit()
# for i in range(1):
# front = [x for x in population if x.rank == i]
# if not front:
# continue
# x, y = np.array([x.data.value for x in front]).T
# plt.scatter(x, y, label=f'{front[0][0]:.3f}')
front = population
x, y = np.array([x.data.value for x in front]).T
plt.scatter(x, y, label=f'{front[0][0]:.3f}')
# plt.legend()
# if epoch >= 10:
# plt.show()
# return
plt.pause(10 / imax)
plt.show()
def comp_main():
def read(files):
for file in files:
name = re.search(r'out_\d+', file)[0]
print(name, end=' \r')
yield name, np.load(file)
for d in ut.fsort(os.listdir('.')):
loc = f'{d}/result'
if not os.path.isdir(loc):
continue
try:
with ut.chdir(loc):
# if any(ut.iglobm('*.npz')):
# continue
if any(ut.iglobm('*.npy')):
print(loc)
for i in range(10):
out = f'out_{i:03d}.npz'
if os.path.isfile(out):
continue
l = i * 100
u = (i + 1) * 100 if i < 9 else 1001
files = [f'out_{j:04d}.plt.npy' for j in range(l, u)]
if not all(map(os.path.isfile, files)):
continue
print(f'{l}=>{u}', ' ' * 20, end='\r')
h = dict(read(files))
try:
np.savez_compressed(out, **h)
except:
if os.path.isfile(out):
os.remove(out)
raise
ck = np.load(out)
for nm in ck.files:
f = f'{nm}.plt.npy'
if os.path.isfile(f):
os.remove(f)
except OSError:
print('OSError')
time.sleep(10)
continue
def get_model(out):
# モデル読み込み
# model_cls = {'nsga2':NSGA2, 'moead':MOEAD}[model]
files = ut.fsort(glob.glob(os.path.join(out, f'*epoch*.pkl')))
for i, file in enumerate(files):
print(f'[{i}]', file)
print('select file')
n = int(input())
if n == -1:
pass
elif n < 0:
return
file = files[n]
print('file:', file)
env, optimizer, history = ut.load(file)
return env, optimizer, history
def collect_result(dest):
print('collect_result')
odir = 'result'
rdir = '__raw__'
with post_base(dest):
files = ut.fsort(glob.iglob('out_*.plt'))
last = len(files) - 1
if last < 1:
return
os.makedirs(odir, exist_ok=True)
os.makedirs(rdir, exist_ok=True)
for i, file in enumerate(files):
# print(i, end='\r')
ofile = os.path.join(odir, os.path.basename(file) + '.npy')
if not os.path.isfile(ofile):
data = read_plt(file)
np.save(ofile, data)
if i < last:
shutil.move(file, rdir)
def zip_main():
for loc in ut.fsort(ut.iglobm('**/image_*')):
# loc = f'{d}/image_w'
if not os.path.isdir(loc):
continue
with ut.chdir(loc):
file = f'{ut.basename(loc)}.zip'
if os.path.isfile(file):
continue
print(loc)
# subprocess.run('zip image_w *.png', shell=True)
try:
def f_():
for png in ut.fsort(ut.iglobm('*.png')):
print(png, os.path.getsize(png), end='\r')
with open(png, 'rb'):
yield png
pngs = list(f_())
if not pngs:
continue
with ZipFile(file, 'w', compression=ZIP_DEFLATED) as z:
for png in pngs:
z.write(png)
except:
if os.path.isfile(file):
os.remove(file)
raise
assert os.path.isfile(file)
if os.path.isfile(file):
with ZipFile(file) as z:
for png in z.namelist():
if os.path.isfile(png):
print(png)
os.remove(png)
def ga_main2(out='result'):
file = ut.fsort(glob.glob(os.path.join(out, f'epoch*.pickle')))[-1]
optimizer = NSGA2.load(file=file)
population = optimizer[-1]
front = [x for x in population if x.rank == 1]
front.sort(key=attrgetter('data.value'))
for ind in front:
print(ind.value, ind.data.value)
crowdings = [ind.value[1] for ind in front]
fig, axes = plt.subplots(2)
axes[0].plot(crowdings)
x, y = np.array([x.data.value for x in front]).T
im = axes[1].scatter(x, y, c=crowdings, cmap='jet')
# plt.xlim((0, 1))
# plt.ylim((0, 1))
fig.colorbar(im)
plt.show()
def f_():
for png in ut.fsort(ut.iglobm('*.png')):
print(png, os.path.getsize(png), end='\r')
with open(png, 'rb'):
yield png