Revision

**138cd0ccaea183c3b4c17c6bbeafbdf148521170**authored by James Hensman on**08 June 2017, 09:58:17 UTC**, committed by James Hensman on**08 June 2017, 09:58:17 UTC****1 parent**0b3ce9b

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 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
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)
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()
```

Computing file changes ...