Revision 034c39669fbef1e88c7d956f6303f9de154c44aa authored by James Hensman on 30 October 2017, 16:26:13 UTC, committed by GitHub on 30 October 2017, 16:26:13 UTC
Documentation fixes
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.from __future__ import print_function
import numpy as np
import tensorflow as tf
from nose.plugins.attrib import attr
import unittest
import gpflow
from testing.gpflow_testcase import GPflowTestCase
@attr(speed='slow')
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 setUp(self):
with self.test_session():
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.gpr.GPR(
X, Y, kern=gpflow.kernels.RBF(1),
mean_function=gpflow.mean_functions.Constant())
m2 = gpflow.vgp.VGP(
X, Y, gpflow.kernels.RBF(1), likelihood=gpflow.likelihoods.Gaussian(),
mean_function=gpflow.mean_functions.Constant())
m3 = gpflow.svgp.SVGP(
X, Y, gpflow.kernels.RBF(1),
likelihood=gpflow.likelihoods.Gaussian(),
Z=X.copy(),
q_diag=False,
mean_function=gpflow.mean_functions.Constant())
m3.Z.fixed = True
m4 = gpflow.svgp.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.Z.fixed = True
m5 = gpflow.sgpr.SGPR(
X, Y, gpflow.kernels.RBF(1),
Z=X.copy(),
mean_function=gpflow.mean_functions.Constant())
m5.Z.fixed = True
m6 = gpflow.sgpr.GPRFITC(
X, Y, gpflow.kernels.RBF(1), Z=X.copy(),
mean_function=gpflow.mean_functions.Constant())
m6.Z.fixed = True
self.models = [m1, m2, m3, m4, m5, m6]
for m in self.models:
m.optimize(disp=False, maxiter=300)
# TODO(awav): Instead of this hack the test must be splitted up
print('.') # stop travis timing out
def test_all(self):
with self.test_session():
likelihoods = np.array([
-m._objective(m.get_free_state())[0].squeeze()
for m in self.models])
self.assertTrue(np.allclose(likelihoods, likelihoods[0], 1e-6))
variances, lengthscales = [], []
for m in self.models:
if hasattr(m.kern, 'rbf'):
variances.append(m.kern.rbf.variance.value)
lengthscales.append(m.kern.rbf.lengthscales.value)
else:
variances.append(m.kern.variance.value)
lengthscales.append(m.kern.lengthscales.value)
variances, lengthscales = np.array(variances), np.array(lengthscales)
self.assertTrue(np.allclose(variances, variances[0], 1e-5))
self.assertTrue(np.allclose(lengthscales, lengthscales.mean(), 1e-4))
mu0, var0 = self.models[0].predict_y(self.Xtest)
for m in self.models[1:]:
mu, var = m.predict_y(self.Xtest)
self.assertTrue(np.allclose(mu, mu0, 1e-3))
self.assertTrue(np.allclose(var, var0, 1e-4))
class VGPTest(GPflowTestCase):
def test_vgp_vs_svgp(self):
with self.test_session():
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.svgp.SVGP(X, Y, kern, likelihood, X.copy(),
whiten=True, q_diag=False)
m_vgp = gpflow.vgp.VGP(X, Y, kern, likelihood)
q_mu = np.random.randn(N, DY)
q_sqrt = np.random.randn(N, N, DY)
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 np.allclose(L_svgp, L_vgp)
pred_svgp = m_svgp.predict_f(Xs)
pred_vgp = m_vgp.predict_f(Xs)
assert np.allclose(pred_svgp[0], pred_vgp[0])
assert np.allclose(pred_svgp[1], pred_vgp[1])
def test_vgp_vs_opper_archambeau(self):
with self.test_session():
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.vgp.VGP(X, Y, kern, likelihood)
m_vgp_oa = gpflow.vgp.VGP_opper_archambeau(X, Y, kern, likelihood)
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.settings.numerics.jitter_level
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.svgp.SVGP(
X, Y, kern, likelihood, X.copy(),
whiten=False, q_diag=False)
m_svgp_unwhitened.q_mu = mean
m_svgp_unwhitened.q_sqrt = np.transpose(np.linalg.cholesky(var_dnn), [1, 2, 0])
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.transpose(np.linalg.cholesky(var_white_dnn), [1, 2, 0])
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 np.allclose(L_vgp, L_vgp_oa)
assert np.allclose(L_vgp, L_svgp_unwhitened)
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 np.allclose(pred_vgp[0], pred_vgp_oa[0])
assert np.allclose(pred_vgp[0], pred_svgp_unwhitened[0])
assert np.allclose(pred_vgp[1], pred_vgp_oa[1], rtol=1e-4) # jitter?
assert np.allclose(pred_vgp[1], pred_svgp_unwhitened[1], rtol=1e-4)
def test_recompile(self):
with self.test_session():
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.vgp.VGP(X, Y, kern, likelihood)
m_vgp_oa = gpflow.vgp.VGP_opper_archambeau(X, Y, kern, likelihood)
try:
for m in [m_vgp, m_vgp_oa]:
m.optimize(maxiter=1)
m.X = X[:-1, :]
m.Y = Y[:-1, :]
m.optimize(maxiter=1)
except:
assert False, 'array mismatch'
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_session():
vfe = gpflow.sgpr.SGPR(self.X, self.Y, gpflow.kernels.RBF(1), self.X[:10, :].copy())
vfe.optimize()
full = gpflow.gpr.GPR(self.X, self.Y, gpflow.kernels.RBF(1))
full.kern.lengthscales = vfe.kern.lengthscales.value
full.kern.variance = vfe.kern.variance.value
full.likelihood.variance = vfe.likelihood.variance.value
full.compile()
lml_upper = vfe.compute_upper_bound()
lml_vfe = -vfe._objective(vfe.get_free_state())[0]
lml_full = -full._objective(full.get_free_state())[0]
self.assertTrue(lml_upper > lml_full > lml_vfe)
if __name__ == '__main__':
unittest.main()
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