def test_hoist(cs, id, scale_mth=6):
if scale_mth <= 6:
weight = [0.2] * 5
elif scale_mth == 7:
weight = [0.0625, 0.125, 0.25, 0.5, 1.0]
else:
weight = None
model = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
update_enable=True,
rho_delta=0.1,
enable_rho=True,
scale_method=scale_mth,
init_weight=weight)
method_name = "HOIST-xgb-%d-%d" % (scale_mth, id)
model.method_name = method_name
model.runtime_limit = runtime_limit
model.restart_needed = True
model.run()
print(model.get_incumbent(5))
weights = model.get_weights()
np.save('data/weights_%s.npy' % method_name, np.asarray(weights))
return model.get_incumbent(5)
def test_xfhb(cs):
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
p=0.2,
n_workers=n_work,
info_type='Weighted',
update_enable=True)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet")
print(xfhb.get_incumbent(5))
return xfhb.get_incumbent(5)
def test_hoist(cs, id):
# weight = [0.533, 0.267, 0.133, 0.0667, 0.0333]
weight = [0.2, 0.2, 0.2, 0.2, 0.2]
hoist = XFHB(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work,
update_enable=True, rho_delta=0.1, enable_rho=False, init_rho=0.5,
scale_method=6, init_weight=weight)
hoist.run()
method_name = "HOIST-vae-%d" % id
hoist.plot_statistics(method=method_name)
print(hoist.get_incumbent(5))
return hoist.get_incumbent(5)
def test_hoist(cs, id, scale_mth=6):
if scale_mth == 6:
weight = [0.2] * 5
elif scale_mth == 7:
weight = [0.0625, 0.125, 0.25, 0.5, 1.0]
else:
weight = None
# mfes = XFHB(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work,
# update_enable=True, rho_delta=0.1, enable_rho=True,
# scale_method=scale_mth, init_weight=weight)
hoist = XFHB(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work,
update_enable=True, rho_delta=0.1, enable_rho=False, init_rho=0.5,
scale_method=scale_mth, init_weight=weight)
hoist.restart_needed = True
hoist.runtime_limit = args.b
method_id = "HOIST-resnet-%d-%d" % (scale_mth, id)
hoist.set_method_name(method_id)
hoist.run()
result = hoist.get_incumbent(5)
save_result(method_id, result)
return result
def test_hoist(cs, id, scale_mth=6):
if scale_mth == 6:
weight = [0.2]*5
elif scale_mth == 7:
weight = [0.0625, 0.125, 0.25, 0.5, 1.0]
else:
weight = None
# mfes = XFHB(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work,
# update_enable=True, rho_delta=0.1, enable_rho=True,
# scale_method=scale_mth, init_weight=weight)
hoist = XFHB(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work,
update_enable=True, rho_delta=0.1, enable_rho=False, init_rho=0.5,
scale_method=scale_mth, init_weight=weight)
hoist.run()
method_name = "HOIST-cnn-%d-%d" % (scale_mth, id)
hoist.plot_statistics(method=method_name)
print(hoist.get_incumbent(5))
return hoist.get_incumbent(5)
def test_hoist(cs, n_id, update_w=True):
weight = [1.0, 0.5, 0.25, 0.125, 0.125]
hoist = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=update_w,
update_delta=1,
random_mode=False,
init_weight=weight)
hoist.run()
method_name = "HOIST-rl-%d" % n_id
if not update_w:
method_name = "HOIST-rl-no_update-%d" % n_id
hoist.plot_statistics(method=method_name)
result = hoist.get_incumbent(5)
print(result)
return result
def test_hoist(cs, id, scale_mth=6):
if scale_mth <= 6:
weight = [0.2] * 5
elif scale_mth == 7:
weight = [0.0625, 0.125, 0.25, 0.5, 1.0]
else:
weight = None
hoist = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
update_enable=True,
rho_delta=0.1,
enable_rho=True,
scale_method=scale_mth,
init_weight=weight)
hoist.runtime_limit = 60000
method_name = "HOIST-rnn-%d-%d" % (scale_mth, id)
hoist.method_name = method_name
hoist.run()
hoist.plot_statistics(method=method_name)
print(hoist.get_incumbent(5))
weights = hoist.get_weights()
np.save('data/weights_%s.npy' % method_name, np.asarray(weights))
return hoist.get_incumbent(5)
def test_update_rule(cs):
iterations = args.iter_c
for i in range(1, 1 + iterations):
high_start_w = [1.0, 0.9, 0.72, 0.504, 0.3024, 0.1512]
low_start_w = [1.0, 0.6, 0.5, 0.4, 0.4]
weight = [1.0, 0.5, 0.25, 0.125, 0.125]
zero_start = [0.0, 0.0, 0.0, 0.0, 0.0]
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=True,
update_delta=1,
init_weight=weight,
random_mode=False)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-update_w_random_f-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=True,
update_delta=1,
init_weight=weight,
random_mode=True)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-update_w_random_t-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
init_weight=weight)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-not_update_w-%d" % i)
hyperband = Hyperband(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work)
hyperband.set_restart()
hyperband.run()
hyperband.plot_statistics(method="HB-lenet-new-%d" % i)
bohb = BOHB(cs,
train,
maximal_iter,
num_iter=iter_num,
p=0.2,
n_workers=n_work)
bohb.set_restart()
bohb.run()
bohb.plot_statistics(method="BOHB-lenet-new-%d" % i)
def test_methods_mean_variance(cs):
iterations = args.iter_c
for i in range(1, 1 + iterations):
weight = [1.0, 0.5, 0.25, 0.125, 0.125]
bo = SMAC(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work)
bo.run()
bo.plot_statistics(method="BO-lenet-%d" % i)
hyperband = Hyperband(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work)
hyperband.set_restart()
hyperband.run()
hyperband.plot_statistics(method="HB-lenet-%d" % i)
bohb = BOHB(cs,
train,
maximal_iter,
num_iter=iter_num,
p=0.2,
n_workers=n_work)
bohb.set_restart()
bohb.run()
bohb.plot_statistics(method="BOHB-lenet-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
init_weight=weight,
random_mode=False)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-disable_w-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=True,
update_delta=1,
init_weight=weight,
random_mode=False)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-update_w-%d" % i)