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
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
|
from __future__ import annotations
import numpy as np
import pytest
from scipy.optimize import NonlinearConstraint
from bayes_opt import BayesianOptimization, ConstraintModel
@pytest.fixture
def target_function():
return lambda x, y: np.cos(2 * x) * np.cos(y) + np.sin(x)
@pytest.fixture
def constraint_function():
return lambda x, y: np.cos(x) * np.cos(y) - np.sin(x) * np.sin(y)
def test_constraint_property(target_function, constraint_function):
constraint_limit_upper = 0.5
constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit_upper)
pbounds = {"x": (0, 6), "y": (0, 6)}
optimizer = BayesianOptimization(
f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1
)
assert isinstance(optimizer.constraint, ConstraintModel)
assert isinstance(optimizer.space.constraint, ConstraintModel)
def test_single_constraint_upper(target_function, constraint_function):
constraint_limit_upper = 0.5
constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit_upper)
pbounds = {"x": (0, 6), "y": (0, 6)}
optimizer = BayesianOptimization(
f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1
)
optimizer.maximize(init_points=2, n_iter=10)
assert constraint_function(**optimizer.max["params"]) <= constraint_limit_upper
def test_single_constraint_lower(target_function, constraint_function):
constraint_limit_lower = -0.5
constraint = NonlinearConstraint(constraint_function, constraint_limit_lower, np.inf)
pbounds = {"x": (0, 6), "y": (0, 6)}
optimizer = BayesianOptimization(
f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1
)
optimizer.maximize(init_points=2, n_iter=10)
assert constraint_function(**optimizer.max["params"]) >= constraint_limit_lower
def test_single_constraint_lower_upper(target_function, constraint_function):
constraint_limit_lower = -0.5
constraint_limit_upper = 0.5
constraint = NonlinearConstraint(constraint_function, constraint_limit_lower, constraint_limit_upper)
pbounds = {"x": (0, 6), "y": (0, 6)}
optimizer = BayesianOptimization(
f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1
)
assert optimizer.constraint.lb == constraint.lb
assert optimizer.constraint.ub == constraint.ub
optimizer.maximize(init_points=2, n_iter=10)
# Check limits
assert constraint_function(**optimizer.max["params"]) <= constraint_limit_upper
assert constraint_function(**optimizer.max["params"]) >= constraint_limit_lower
# Exclude the last sampled point, because the constraint is not fitted on that.
res = np.array(
[[r["target"], r["constraint"], r["params"]["x"], r["params"]["y"]] for r in optimizer.res[:-1]]
)
xy = res[:, [2, 3]]
x = res[:, 2]
y = res[:, 3]
# Check accuracy of approximation for sampled points
assert constraint_function(x, y) == pytest.approx(optimizer.constraint.approx(xy), rel=1e-5, abs=1e-5)
assert constraint_function(x, y) == pytest.approx(
optimizer.space.constraint_values[:-1], rel=1e-5, abs=1e-5
)
def test_multiple_constraints(target_function):
def constraint_function_2_dim(x, y):
return np.array(
[-np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y), -np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)]
)
constraint_limit_lower = np.array([-np.inf, -np.inf])
constraint_limit_upper = np.array([0.6, 0.6])
conmod = NonlinearConstraint(constraint_function_2_dim, constraint_limit_lower, constraint_limit_upper)
pbounds = {"x": (0, 6), "y": (0, 6)}
optimizer = BayesianOptimization(
f=target_function, constraint=conmod, pbounds=pbounds, verbose=0, random_state=1
)
optimizer.maximize(init_points=2, n_iter=10)
constraint_at_max = constraint_function_2_dim(**optimizer.max["params"])
assert np.all(
(constraint_at_max <= constraint_limit_upper) & (constraint_at_max >= constraint_limit_lower)
)
params = optimizer.res[0]["params"]
x, y = params["x"], params["y"]
assert constraint_function_2_dim(x, y) == pytest.approx(
optimizer.constraint.approx(np.array([x, y])), rel=1e-3, abs=1e-3
)
def test_kwargs_not_the_same(target_function):
def target_function(x, y):
return np.cos(2 * x) * np.cos(y) + np.sin(x)
def constraint_function(a, b):
return np.cos(a) * np.cos(b) - np.sin(a) * np.sin(b)
constraint_limit_upper = 0.5
constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit_upper)
pbounds = {"x": (0, 6), "y": (0, 6)}
optimizer = BayesianOptimization(
f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1
)
with pytest.raises(TypeError, match="Encountered TypeError when evaluating"):
optimizer.maximize(init_points=2, n_iter=10)
def test_lower_less_than_upper(target_function):
def target_function(x, y):
return np.cos(2 * x) * np.cos(y) + np.sin(x)
def constraint_function_2_dim(x, y):
return np.array(
[-np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y), -np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)]
)
constraint_limit_lower = np.array([0.6, -np.inf])
constraint_limit_upper = np.array([0.3, 0.6])
conmod = NonlinearConstraint(constraint_function_2_dim, constraint_limit_lower, constraint_limit_upper)
pbounds = {"x": (0, 6), "y": (0, 6)}
with pytest.raises(ValueError):
BayesianOptimization(f=target_function, constraint=conmod, pbounds=pbounds, verbose=0, random_state=1)
def test_null_constraint_function():
constraint = ConstraintModel(None, np.array([0, 0]), np.array([1, 1]))
with pytest.raises(ValueError, match="No constraint function was provided."):
constraint.eval()
|
