https://github.com/GPflow/GPflow
Revision 80f6d6fa511b533a219e6241be4cb114a0cb7cc2 authored by Artem Artemev on 19 October 2018, 13:20:05 UTC, committed by GitHub on 19 October 2018, 13:20:05 UTC
1 parent 2c4cf39
Tip revision: 80f6d6fa511b533a219e6241be4cb114a0cb7cc2 authored by Artem Artemev on 19 October 2018, 13:20:05 UTC
Release v1.3.0 (#869)
Release v1.3.0 (#869)
Tip revision: 80f6d6f
test_method_equivalence.py
# Copyright 2016 the GPflow authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
import numpy as np
from numpy.testing import assert_allclose
import gpflow
from gpflow.test_util import GPflowTestCase
class TestEquivalence(GPflowTestCase):
"""
With a Gaussian likelihood, and inducing points (where appropriate)
positioned at the data, many of the gpflow methods are equivalent (perhaps
subject to some optimization).
Here, we make 5 models that should be the same, and make sure some
similarites hold. The models are:
1) GP Regression
2) Variational GP (with the likelihood set to Gaussian)
3) Sparse variational GP (likelihood is Gaussian, inducing points
at the data)
4) Sparse variational GP (as above, but with the whitening rotation
of the inducing variables)
5) Sparse variational GP Regression (as above, but there the inducing
variables are 'collapsed' out, as in Titsias 2009)
"""
def prepare(self):
rng = np.random.RandomState(0)
X = rng.rand(20, 1) * 10
Y = np.sin(X) + 0.9 * np.cos(X * 1.6) + rng.randn(*X.shape) * 0.8
Y = np.tile(Y, 2) # two identical columns
self.Xtest = rng.rand(10, 1) * 10
m1 = gpflow.models.GPR(
X, Y, kern=gpflow.kernels.RBF(1),
mean_function=gpflow.mean_functions.Constant())
m2 = gpflow.models.VGP(
X, Y, gpflow.kernels.RBF(1), likelihood=gpflow.likelihoods.Gaussian(),
mean_function=gpflow.mean_functions.Constant())
m3 = gpflow.models.SVGP(
X, Y, gpflow.kernels.RBF(1),
likelihood=gpflow.likelihoods.Gaussian(),
Z=X.copy(),
q_diag=False,
mean_function=gpflow.mean_functions.Constant())
m3.feature.trainable = False
m4 = gpflow.models.SVGP(
X, Y, gpflow.kernels.RBF(1),
likelihood=gpflow.likelihoods.Gaussian(),
Z=X.copy(), q_diag=False, whiten=True,
mean_function=gpflow.mean_functions.Constant())
m4.feature.trainable = False
m5 = gpflow.models.SGPR(
X, Y, gpflow.kernels.RBF(1),
Z=X.copy(),
mean_function=gpflow.mean_functions.Constant())
m5.feature.trainable = False
m6 = gpflow.models.GPRFITC(
X, Y, gpflow.kernels.RBF(1), Z=X.copy(),
mean_function=gpflow.mean_functions.Constant())
m6.feature.trainable = False
return [m1, m2, m3, m4, m5, m6]
def test_all(self):
with self.test_context() as session:
models = self.prepare()
likelihoods = []
for m in models:
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=300)
neg_obj = tf.negative(m.objective)
likelihoods.append(session.run(neg_obj).squeeze())
assert_allclose(likelihoods, likelihoods[0], rtol=1e-6)
variances, lengthscales = [], []
for m in models:
if hasattr(m.kern, 'rbf'):
variances.append(m.kern.rbf.variance.read_value())
lengthscales.append(m.kern.rbf.lengthscales.read_value())
else:
variances.append(m.kern.variance.read_value())
lengthscales.append(m.kern.lengthscales.read_value())
variances, lengthscales = np.array(variances), np.array(lengthscales)
assert_allclose(variances, variances[0], 1e-5)
assert_allclose(lengthscales, lengthscales.mean(), 1e-4)
mu0, var0 = models[0].predict_y(self.Xtest)
for i, m in enumerate(models[1:]):
mu, var = m.predict_y(self.Xtest)
assert_allclose(mu, mu0, 1e-3)
assert_allclose(var, var0, 1e-4)
class VGPTest(GPflowTestCase):
def test_vgp_vs_svgp(self):
with self.test_context():
N, Ns, DX, DY = 100, 10, 2, 2
np.random.seed(1)
X = np.random.randn(N, DX)
Xs = np.random.randn(Ns, DX)
Y = np.random.randn(N, DY)
kern = gpflow.kernels.Matern52(DX)
likelihood = gpflow.likelihoods.StudentT()
m_svgp = gpflow.models.SVGP(
X, Y, kern, likelihood, X.copy(), whiten=True, q_diag=False)
m_vgp = gpflow.models.VGP(X, Y, kern, likelihood)
m_svgp.compile()
m_vgp.compile()
q_mu = np.random.randn(N, DY)
q_sqrt = np.random.randn(DY, N, N)
m_svgp.q_mu = q_mu
m_svgp.q_sqrt = q_sqrt
m_vgp.q_mu = q_mu
m_vgp.q_sqrt = q_sqrt
L_svgp = m_svgp.compute_log_likelihood()
L_vgp = m_vgp.compute_log_likelihood()
assert_allclose(L_svgp, L_vgp, rtol=1e-2)
pred_svgp = m_svgp.predict_f(Xs)
pred_vgp = m_vgp.predict_f(Xs)
assert_allclose(pred_svgp[0], pred_vgp[0])
assert_allclose(pred_svgp[1], pred_vgp[1])
def test_vgp_vs_opper_archambeau(self):
with self.test_context():
N, Ns, DX, DY = 100, 10, 2, 2
np.random.seed(1)
X = np.random.randn(N, DX)
Xs = np.random.randn(Ns, DX)
Y = np.random.randn(N, DY)
kern = gpflow.kernels.Matern52(DX)
likelihood = gpflow.likelihoods.StudentT()
m_vgp = gpflow.models.VGP(X, Y, kern, likelihood)
m_vgp_oa = gpflow.models.VGP_opper_archambeau(X, Y, kern, likelihood)
m_vgp.compile()
m_vgp_oa.compile()
q_alpha = np.random.randn(N, DX)
q_lambda = np.random.randn(N, DX) ** 2
m_vgp_oa.q_alpha = q_alpha
m_vgp_oa.q_lambda = q_lambda
K = kern.compute_K_symm(X) + np.eye(N) * gpflow.settings.jitter
L = np.linalg.cholesky(K)
L_inv = np.linalg.inv(L)
K_inv = np.linalg.inv(K)
mean = K.dot(q_alpha)
prec_dnn = K_inv[None, :, :] + np.array([np.diag(l ** 2) for l in q_lambda.T])
var_dnn = np.linalg.inv(prec_dnn)
m_svgp_unwhitened = gpflow.models.SVGP(
X, Y, kern, likelihood, X.copy(),
whiten=False, q_diag=False)
m_svgp_unwhitened.q_mu = mean
m_svgp_unwhitened.q_sqrt = np.linalg.cholesky(var_dnn)
m_svgp_unwhitened.compile()
mean_white_nd = L_inv.dot(mean)
var_white_dnn = np.einsum('nN,dNM,mM->dnm', L_inv, var_dnn, L_inv)
q_sqrt_nnd = np.linalg.cholesky(var_white_dnn)
m_vgp.q_mu = mean_white_nd
m_vgp.q_sqrt = q_sqrt_nnd
L_vgp = m_vgp.compute_log_likelihood()
L_svgp_unwhitened = m_svgp_unwhitened.compute_log_likelihood()
L_vgp_oa = m_vgp_oa.compute_log_likelihood()
assert_allclose(L_vgp, L_vgp_oa, rtol=1e-2)
assert_allclose(L_vgp, L_svgp_unwhitened, rtol=1e-2)
pred_vgp = m_vgp.predict_f(Xs)
pred_svgp_unwhitened = m_svgp_unwhitened.predict_f(Xs)
pred_vgp_oa = m_vgp_oa.predict_f(Xs)
assert_allclose(pred_vgp[0], pred_vgp_oa[0])
assert_allclose(pred_vgp[0], pred_svgp_unwhitened[0])
assert_allclose(pred_vgp[1], pred_vgp_oa[1], rtol=1e-4) # jitter?
assert_allclose(pred_vgp[1], pred_svgp_unwhitened[1], rtol=1e-4)
#def test_recompile(self):
# with self.test_context():
# N, DX, DY = 100, 2, 2
# np.random.seed(1)
# X = np.random.randn(N, DX)
# Y = np.random.randn(N, DY)
# kern = gpflow.kernels.Matern52(DX)
# likelihood = gpflow.likelihoods.StudentT()
# m_vgp = gpflow.models.VGP(X, Y, kern, likelihood)
# m_vgp_oa = gpflow.models.VGP_opper_archambeau(X, Y, kern, likelihood)
# for m in [m_vgp, m_vgp_oa]:
# m.compile()
# opt = gpflow.train.ScipyOptimizer()
# opt.minimize(m, maxiter=1)
# m.X = X[:-1, :]
# m.Y = Y[:-1, :]
# opt.minimize(m, maxiter=1)
class TestUpperBound(GPflowTestCase):
"""
Test for upper bound for regression marginal likelihood
"""
def setUp(self):
self.X = np.random.rand(100, 1)
self.Y = np.sin(1.5 * 2 * np.pi * self.X) + np.random.randn(*self.X.shape) * 0.1
def test_few_inducing_points(self):
with self.test_context() as session:
vfe = gpflow.models.SGPR(self.X, self.Y, gpflow.kernels.RBF(1), self.X[:10, :].copy())
opt = gpflow.train.ScipyOptimizer()
opt.minimize(vfe)
full = gpflow.models.GPR(self.X, self.Y, gpflow.kernels.RBF(1))
full.kern.lengthscales = vfe.kern.lengthscales.read_value()
full.kern.variance = vfe.kern.variance.read_value()
full.likelihood.variance = vfe.likelihood.variance.read_value()
lml_upper = vfe.compute_upper_bound()
lml_vfe = - session.run(vfe.objective)
lml_full = - session.run(full.objective)
self.assertTrue(lml_upper > lml_full > lml_vfe)
if __name__ == '__main__':
tf.test.main()
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