-
Notifications
You must be signed in to change notification settings - Fork 3
/
demo_synfunc_1d_C6_b1_5_10.py
97 lines (80 loc) · 3.71 KB
/
demo_synfunc_1d_C6_b1_5_10.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
import numpy as np
import pickle
import timeit
import datetime
from hyperopt import hp
from mab_cons.OnehotEnconding import OnehotEncoding
from mab_cons.MerchanLobato import MerchanLobato
from mab_cons.SMAC import SMAC
from mab_cons.TPE import TPE
from mab_cons.BanditBO import BanditBO
import test_functions.synfunc_1d
np.random.seed(123)
test_case = "1d_C6"
f_bo = test_functions.synfunc_1d.obj_func_bo
f_tpe = test_functions.synfunc_1d.obj_func_tpe
c_bound_dim = 6
x_bound_dim = 1
x_bound = [-2.0, 10.0]
trials = 10 # 10
n_arm = c_bound_dim
n_dim = x_bound_dim
budget = 40 # 40
batch_list = [1] # [1, 5, 10]
n_init = 2
# BanditBO
bounds_mab = [{"name": "x{}".format(d+1), "type": "continuous", "domain": (x_bound[0], x_bound[1])}
for d in range(x_bound_dim)]
# OnehotEncoding and SMAC
all_c = tuple(range(c_bound_dim))
bounds_gpy_c = [{'name': 'x1', 'type': 'categorical', 'domain': all_c}]
bounds_gpy_x = [{"name": "x{}".format(d+2), "type": "continuous", "domain": (x_bound[0], x_bound[1])}
for d in range(x_bound_dim)]
bounds_gpy = list(np.append(bounds_gpy_c, bounds_gpy_x))
# MerchanLobato
bounds_categorical_c = [{"name": "x{}".format(d+1), "type": "continuous", "domain": (0, 1)}
for d in range(c_bound_dim)]
bounds_categorical_x = [{"name": "x{}".format(d+c_bound_dim+1), "type": "continuous", "domain": (x_bound[0], x_bound[1])}
for d in range(x_bound_dim)]
bounds_categorical = list(np.append(bounds_categorical_c, bounds_categorical_x))
# TPE
all_c = list(range(c_bound_dim))
bounds_tpe_c = {'x1': hp.choice('x1', all_c)}
bounds_tpe_x = {'x{}'.format(d+2): hp.uniform('x{}'.format(d+2), x_bound[0], x_bound[1]) for d in range(x_bound_dim)}
bounds_tpe = {**bounds_tpe_c, **bounds_tpe_x}
start_date_time = datetime.datetime.now()
start_time = timeit.default_timer()
# save models to files
batch_name = "_".join(map(str, batch_list))
# baselines
onehot = OnehotEncoding(objfn=f_bo, n_init=n_init, bounds=bounds_gpy, acq_type='LCB', n_arm=n_arm,
dataset=test_case, rand_seed=108)
onehot.runoptimBatchList(trials, budget, batch_list)
with open("{}_onehot_b{}.pickle".format(test_case, batch_name), "wb") as f:
pickle.dump(onehot, f)
merchanlobato = MerchanLobato(objfn=f_bo, n_init=n_init, bounds=bounds_categorical, acq_type='LCB', n_arm=n_arm,
dataset=test_case, rand_seed=108)
merchanlobato.runoptimBatchList(trials, budget, batch_list)
with open("{}_merchanlobato_b{}.pickle".format(test_case, batch_name), "wb") as f:
pickle.dump(merchanlobato, f)
smac = SMAC(objfn=f_bo, n_init=n_init, bounds=bounds_gpy, acq_type='LCB', n_arm=n_arm,
dataset=test_case, rand_seed=108)
smac.runoptimBatchList(trials, budget, batch_list)
with open("{}_smac_b{}.pickle".format(test_case, batch_name), "wb") as f:
pickle.dump(smac, f)
tpe = TPE(objfn=f_tpe, n_init=n_init, bounds=bounds_tpe, acq_type='', n_arm=n_arm,
f_type="func", dataset=test_case, rand_seed=108)
tpe.runoptimBatchList(trials, budget, batch_list)
with open("{}_tpe_b{}.pickle".format(test_case, batch_name), "wb") as f:
pickle.dump(tpe, f)
# proposed method
ts_draw = "set" # func, set
banditbo = BanditBO(objfn=f_bo, n_init=n_init, bounds=bounds_mab, acq_type='TS', n_arm=n_arm,
norm_y=True, ts_draw=ts_draw, dataset=test_case, rand_seed=108)
banditbo.runoptimBatchList(trials, budget, batch_list)
with open("{}_banditbo_b{}.pickle".format(test_case, batch_name), "wb") as f:
pickle.dump(banditbo, f)
end_date_time = datetime.datetime.now()
end_time = timeit.default_timer()
print("start date time: {} and end date time: {}".format(start_date_time, end_date_time))
print("runtime: {}(s)".format(round(end_time-start_time, 2)))