/
test_linear_truncated_fidelity.py
217 lines (197 loc) · 9.18 KB
/
test_linear_truncated_fidelity.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
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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import torch
from botorch.exceptions import UnsupportedError
from botorch.models.kernels.linear_truncated_fidelity import (
LinearTruncatedFidelityKernel,
)
from botorch.utils.testing import BotorchTestCase
from gpytorch.kernels.matern_kernel import MaternKernel
from gpytorch.kernels.rbf_kernel import RBFKernel
from gpytorch.priors.torch_priors import GammaPrior, NormalPrior
from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
class TestLinearTruncatedFidelityKernel(BotorchTestCase, BaseKernelTestCase):
def create_kernel_no_ard(self, **kwargs):
return LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2], dimension=3, **kwargs
)
def create_data_no_batch(self):
return torch.rand(50, 10)
def create_data_single_batch(self):
return torch.rand(2, 50, 3)
def create_data_double_batch(self):
return torch.rand(3, 2, 50, 3)
def test_compute_linear_truncated_kernel_no_batch(self):
x1 = torch.tensor([1, 0.1, 0.2, 2, 0.3, 0.4], dtype=torch.float).view(2, 3)
x2 = torch.tensor([3, 0.5, 0.6, 4, 0.7, 0.8], dtype=torch.float).view(2, 3)
t_1 = torch.tensor([0.3584, 0.1856, 0.2976, 0.1584], dtype=torch.float).view(
2, 2
)
for nu, fidelity_dims in itertools.product({0.5, 1.5, 2.5}, ([2], [1, 2])):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims, dimension=3, nu=nu
)
kernel.power = 1
if len(fidelity_dims) > 1:
active_dimsM = [0]
t_2 = torch.tensor(
[0.4725, 0.2889, 0.4025, 0.2541], dtype=torch.float
).view(2, 2)
t_3 = torch.tensor(
[0.1685, 0.0531, 0.1168, 0.0386], dtype=torch.float
).view(2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu, active_dims=active_dimsM)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
# test diagonal mode
res_diag = kernel(x1, x2, diag=True)
self.assertLess(torch.norm(res_diag - actual.diag()), 1e-4)
# make sure that we error out if last_dim_is_batch=True
with self.assertRaises(NotImplementedError):
kernel(x1, x2, diag=True, last_dim_is_batch=True)
def test_compute_linear_truncated_kernel_with_batch(self):
x1 = torch.tensor(
[1, 0.1, 0.2, 3, 0.3, 0.4, 5, 0.5, 0.6, 7, 0.7, 0.8], dtype=torch.float
).view(2, 2, 3)
x2 = torch.tensor(
[2, 0.8, 0.7, 4, 0.6, 0.5, 6, 0.4, 0.3, 8, 0.2, 0.1], dtype=torch.float
).view(2, 2, 3)
t_1 = torch.tensor(
[0.2736, 0.44, 0.2304, 0.36, 0.3304, 0.3816, 0.1736, 0.1944],
dtype=torch.float,
).view(2, 2, 2)
batch_shape = torch.Size([2])
for nu, fidelity_dims in itertools.product({0.5, 1.5, 2.5}, ([2], [1, 2])):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims, dimension=3, nu=nu, batch_shape=batch_shape
)
kernel.power = 1
if len(fidelity_dims) > 1:
active_dimsM = [0]
t_2 = torch.tensor(
[0.0527, 0.167, 0.0383, 0.1159, 0.1159, 0.167, 0.0383, 0.0527],
dtype=torch.float,
).view(2, 2, 2)
t_3 = torch.tensor(
[0.1944, 0.3816, 0.1736, 0.3304, 0.36, 0.44, 0.2304, 0.2736],
dtype=torch.float,
).view(2, 2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(
nu=nu, active_dims=active_dimsM, batch_shape=batch_shape
)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
# test diagonal mode
res_diag = kernel(x1, x2, diag=True)
self.assertLess(
torch.norm(res_diag - torch.diagonal(actual, dim1=-1, dim2=-2)), 1e-4
)
# make sure that we error out if last_dim_is_batch=True
with self.assertRaises(NotImplementedError):
kernel(x1, x2, diag=True, last_dim_is_batch=True)
def test_initialize_lengthscale_prior(self):
kernel = LinearTruncatedFidelityKernel(fidelity_dims=[1, 2], dimension=3)
self.assertTrue(
isinstance(kernel.covar_module_unbiased.lengthscale_prior, GammaPrior)
)
self.assertTrue(
isinstance(kernel.covar_module_biased.lengthscale_prior, GammaPrior)
)
kernel2 = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2],
dimension=3,
lengthscale_prior_unbiased=NormalPrior(1, 1),
)
self.assertTrue(
isinstance(kernel2.covar_module_unbiased.lengthscale_prior, NormalPrior)
)
kernel2 = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2],
dimension=3,
lengthscale_prior_biased=NormalPrior(1, 1),
)
self.assertTrue(
isinstance(kernel2.covar_module_biased.lengthscale_prior, NormalPrior)
)
def test_initialize_power_prior(self):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2], dimension=3, power_prior=NormalPrior(1, 1)
)
self.assertTrue(isinstance(kernel.power_prior, NormalPrior))
def test_initialize_power(self):
kernel = LinearTruncatedFidelityKernel(fidelity_dims=[1, 2], dimension=3)
kernel.initialize(power=1)
actual_value = torch.tensor(1, dtype=torch.float).view_as(kernel.power)
self.assertLess(torch.norm(kernel.power - actual_value), 1e-5)
def test_initialize_power_batch(self):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2], dimension=3, batch_shape=torch.Size([2])
)
power_init = torch.tensor([1, 2], dtype=torch.float)
kernel.initialize(power=power_init)
actual_value = power_init.view_as(kernel.power)
self.assertLess(torch.norm(kernel.power - actual_value), 1e-5)
def test_raise_init_errors(self):
with self.assertRaises(UnsupportedError):
LinearTruncatedFidelityKernel(fidelity_dims=[2])
with self.assertRaises(UnsupportedError):
LinearTruncatedFidelityKernel(fidelity_dims=[0, 1, 2], dimension=3)
with self.assertRaises(ValueError):
LinearTruncatedFidelityKernel(fidelity_dims=[2, 2], dimension=3)
with self.assertRaises(ValueError):
LinearTruncatedFidelityKernel(fidelity_dims=[2], dimension=2, nu=1)
def test_active_dims_list(self):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2], dimension=10, active_dims=[0, 2, 4, 6]
)
x = self.create_data_no_batch()
covar_mat = kernel(x).evaluate_kernel().evaluate()
kernel_basic = LinearTruncatedFidelityKernel(fidelity_dims=[1, 2], dimension=4)
covar_mat_actual = kernel_basic(x[:, [0, 2, 4, 6]]).evaluate_kernel().evaluate()
self.assertLess(
torch.norm(covar_mat - covar_mat_actual) / covar_mat_actual.norm(), 1e-4
)
def test_active_dims_range(self):
active_dims = list(range(3, 9))
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2], dimension=10, active_dims=active_dims
)
x = self.create_data_no_batch()
covar_mat = kernel(x).evaluate_kernel().evaluate()
kernel_basic = LinearTruncatedFidelityKernel(fidelity_dims=[1, 2], dimension=6)
covar_mat_actual = kernel_basic(x[:, active_dims]).evaluate_kernel().evaluate()
self.assertLess(
torch.norm(covar_mat - covar_mat_actual) / covar_mat_actual.norm(), 1e-4
)
def test_initialize_covar_module(self):
kernel = LinearTruncatedFidelityKernel(fidelity_dims=[1, 2], dimension=3)
self.assertTrue(isinstance(kernel.covar_module_unbiased, MaternKernel))
self.assertTrue(isinstance(kernel.covar_module_biased, MaternKernel))
kernel.covar_module_unbiased = RBFKernel()
kernel.covar_module_biased = RBFKernel()
self.assertTrue(isinstance(kernel.covar_module_unbiased, RBFKernel))
self.assertTrue(isinstance(kernel.covar_module_biased, RBFKernel))
kernel2 = LinearTruncatedFidelityKernel(
fidelity_dims=[1, 2],
dimension=3,
covar_module_unbiased=RBFKernel(),
covar_module_biased=RBFKernel(),
)
self.assertTrue(isinstance(kernel2.covar_module_unbiased, RBFKernel))
self.assertTrue(isinstance(kernel2.covar_module_biased, RBFKernel))