swh:1:snp:636001a7d28e32091113da06cec462052713b3b6
Tip revision: 1da68b94dc43d3fe9d30badab70d05a29c50c163 authored by Alexis Boukouvalas on 16 October 2016, 09:05:20 UTC
Merge branch 'master' into gplvm
Merge branch 'master' into gplvm
Tip revision: 1da68b9
test_method_equivalence.py
from __future__ import print_function
import GPflow
import numpy as np
import unittest
import tensorflow as tf
class TestEquivalence(unittest.TestCase):
"""
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):
tf.reset_default_graph()
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
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)
print('.') # stop travis timing out
def test_all(self):
likelihoods = np.array([-m._objective(m.get_free_state())[0].squeeze() for m in self.models])
self.assertTrue(np.allclose(likelihoods, likelihoods[0], 1e-2))
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-3))
self.assertTrue(np.allclose(lengthscales, lengthscales.mean(), 1e-2))
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-2))
self.assertTrue(np.allclose(var, var0, 1e-2))
if __name__ == '__main__':
unittest.main()