forked from ziatdinovmax/gpax
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_hskgp.py
148 lines (122 loc) · 4.87 KB
/
test_hskgp.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
import sys
import pytest
import numpy as onp
import jax.numpy as jnp
import jax
import numpyro
import numpyro.distributions as dist
from numpy.testing import assert_equal, assert_array_equal, assert_
sys.path.insert(0, "../gpax/")
from gpax.models.hskgp import VarNoiseGP
from gpax.utils import get_keys
def get_dummy_data(unsqueeze=False):
X = onp.linspace(1, 2, 8) + 0.1 * onp.random.randn(8,)
y = (10 * X**2)
if unsqueeze:
X = X[:, None]
return jnp.array(X), jnp.array(y)
def noise_fn(x, params):
return params["a"] + params["b"]*x
def noise_fn_prior():
a = numpyro.sample("a", dist.Normal(0, 1))
b = numpyro.sample("b", dist.Normal(0, 1))
return {"a": a, "b": b}
@pytest.mark.parametrize("noise_kernel", ['RBF', 'Matern'])
def test_fit(noise_kernel):
rng_key = get_keys()[0]
X, y = get_dummy_data()
m = VarNoiseGP(1, 'RBF', noise_kernel=noise_kernel)
m.fit(rng_key, X, y, num_warmup=10, num_samples=10)
assert m.mcmc is not None
def test_fit_with_custom_noise_lscale():
rng_key = get_keys()[0]
X, y = get_dummy_data()
m = VarNoiseGP(1, 'RBF', noise_lengthscale_prior_dist=dist.HalfNormal(1))
m.fit(rng_key, X, y, num_warmup=10, num_samples=10)
assert m.mcmc is not None
def test_fit_with_noise_mean_fn():
rng_key = get_keys()[0]
X, y = get_dummy_data()
m = VarNoiseGP(1, 'RBF', noise_mean_fn=noise_fn, noise_mean_fn_prior=noise_fn_prior)
m.fit(rng_key, X, y, num_warmup=10, num_samples=10)
assert m.mcmc is not None
def test_fit_with_noise_and_regular_mean_fn():
rng_key = get_keys()[0]
X, y = get_dummy_data()
m = VarNoiseGP(1, 'RBF', mean_fn = lambda x: 8*x**2,
noise_mean_fn=noise_fn, noise_mean_fn_prior=noise_fn_prior)
m.fit(rng_key, X, y, num_warmup=10, num_samples=10)
assert m.mcmc is not None
def test_get_mvn_posterior():
X, y = get_dummy_data(unsqueeze=True)
X_test, _ = get_dummy_data(unsqueeze=True)
params = {"k_length": jnp.array([1.0]),
"k_scale": jnp.array(1.0),
"noise": jnp.array(0.1),
"k_noise_length": jnp.array(0.5),
"k_noise_scale": jnp.array(1.0),
"log_var": jnp.ones(len(X))}
m = VarNoiseGP(1, 'RBF', noise_kernel='RBF')
m.X_train = X
m.y_train = y
mean, cov = m.get_mvn_posterior(X_test, params)
assert isinstance(mean, jnp.ndarray)
assert isinstance(cov, jnp.ndarray)
assert_equal(mean.shape, (X_test.shape[0],))
assert_equal(cov.shape, (X_test.shape[0], X_test.shape[0]))
def test_get_mvn_posterior_with_mean_fn():
X, y = get_dummy_data(unsqueeze=True)
X_test, _ = get_dummy_data(unsqueeze=True)
params = {"k_length": jnp.array([1.0]),
"k_scale": jnp.array(1.0),
"noise": jnp.array(0.1),
"k_noise_length": jnp.array(0.5),
"k_noise_scale": jnp.array(1.0),
"log_var": jnp.ones(len(X)),
"a": jnp.array(1.0),
"b": jnp.array(1.0)
}
m = VarNoiseGP(1, 'RBF', noise_kernel='RBF', noise_mean_fn=noise_fn, noise_mean_fn_prior=noise_fn_prior)
m.X_train = X
m.y_train = y
mean, cov = m.get_mvn_posterior(X_test, params)
assert isinstance(mean, jnp.ndarray)
assert isinstance(cov, jnp.ndarray)
assert_equal(mean.shape, (X_test.shape[0],))
assert_equal(cov.shape, (X_test.shape[0], X_test.shape[0]))
def test_get_mvn_posterior_with_noise_and_regular_mean_fn():
X, y = get_dummy_data(unsqueeze=True)
X_test, _ = get_dummy_data(unsqueeze=True)
params = {"k_length": jnp.array([1.0]),
"k_scale": jnp.array(1.0),
"noise": jnp.array(0.1),
"k_noise_length": jnp.array(0.5),
"k_noise_scale": jnp.array(1.0),
"log_var": jnp.ones(len(X)),
"a": jnp.array(1.0),
"b": jnp.array(1.0)
}
m = VarNoiseGP(1, 'RBF', noise_kernel='RBF',
mean_fn = lambda x: 8*x**2,
noise_mean_fn=noise_fn, noise_mean_fn_prior=noise_fn_prior)
m.X_train = X
m.y_train = y
mean, cov = m.get_mvn_posterior(X_test, params)
assert isinstance(mean, jnp.ndarray)
assert isinstance(cov, jnp.ndarray)
assert_equal(mean.shape, (X_test.shape[0],))
assert_equal(cov.shape, (X_test.shape[0], X_test.shape[0]))
def test_get_noise_samples():
rng_key = get_keys()[0]
X, y = get_dummy_data()
m = VarNoiseGP(1, 'RBF')
m.fit(rng_key, X, y, num_warmup=10, num_samples=10)
noise = m.get_data_var_samples()
assert_(isinstance(noise, jnp.ndarray))
def test_get_noise_samples_with_mean_fn():
rng_key = get_keys()[0]
X, y = get_dummy_data()
m = VarNoiseGP(1, 'RBF', noise_mean_fn=noise_fn, noise_mean_fn_prior=noise_fn_prior)
m.fit(rng_key, X, y, num_warmup=10, num_samples=10)
noise = m.get_data_var_samples()
assert_(isinstance(noise, jnp.ndarray))