https://github.com/GPflow/GPflow
Revision cf629d5d91a005fe8bdf72791eb8cda0374f2bee authored by James Hensman on 29 November 2017, 00:08:29 UTC, committed by GitHub on 29 November 2017, 00:08:29 UTC
Mention Python 3 in README.md
Tip revision: cf629d5d91a005fe8bdf72791eb8cda0374f2bee authored by James Hensman on 29 November 2017, 00:08:29 UTC
Merge pull request #581 from GPflow/readme-py3
Merge pull request #581 from GPflow/readme-py3
Tip revision: cf629d5
test_gplvm.py
# Copyright 2017 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 tensorflow as tf
import numpy as np
import gpflow
from gpflow.test_util import GPflowTestCase
from gpflow import ekernels
from gpflow import kernels
np.random.seed(0)
class TestGPLVM(GPflowTestCase):
def setUp(self):
# data
self.N = 20 # number of data points
D = 5 # data dimension
self.rng = np.random.RandomState(1)
self.Y = self.rng.randn(self.N, D)
# model
self.Q = 2 # latent dimensions
def test_optimise(self):
with self.test_context():
m = gpflow.models.GPLVM(self.Y, self.Q)
linit = m.compute_log_likelihood()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=2)
self.assertTrue(m.compute_log_likelihood() > linit)
def test_otherkernel(self):
with self.test_context():
k = kernels.PeriodicKernel(self.Q)
XInit = self.rng.rand(self.N, self.Q)
m = gpflow.models.GPLVM(self.Y, self.Q, XInit, k)
linit = m.compute_log_likelihood()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=2)
self.assertTrue(m.compute_log_likelihood() > linit)
class TestBayesianGPLVM(GPflowTestCase):
def setUp(self):
# data
self.N = 20 # number of data points
self.D = 5 # data dimension
self.rng = np.random.RandomState(1)
self.Y = self.rng.randn(self.N, self.D)
# model
self.M = 10 # inducing points
def test_1d(self):
with self.test_context():
Q = 1 # latent dimensions
k = ekernels.RBF(Q)
Z = np.linspace(0, 1, self.M)
Z = np.expand_dims(Z, Q) # inducing points
m = gpflow.models.BayesianGPLVM(
X_mean=np.zeros((self.N, Q)),
X_var=np.ones((self.N, Q)),
Y=self.Y,
kern=k,
M=self.M,
Z=Z)
linit = m.compute_log_likelihood()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=2)
self.assertTrue(m.compute_log_likelihood() > linit)
def test_2d(self):
with self.test_context():
# test default Z on 2_D example
Q = 2 # latent dimensions
X_mean = gpflow.models.PCA_reduce(self.Y, Q)
k = ekernels.RBF(Q, ARD=False)
m = gpflow.models.BayesianGPLVM(
X_mean=X_mean,
X_var=np.ones((self.N, Q)),
Y=self.Y,
kern=k,
M=self.M)
linit = m.compute_log_likelihood()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=2)
self.assertTrue(m.compute_log_likelihood() > linit)
# test prediction
Xtest = self.rng.randn(10, Q)
mu_f, var_f = m.predict_f(Xtest)
mu_fFull, var_fFull = m.predict_f_full_cov(Xtest)
self.assertTrue(np.allclose(mu_fFull, mu_f))
# check full covariance diagonal
for i in range(self.D):
self.assertTrue(np.allclose(var_f[:, i], np.diag(var_fFull[:, :, i])))
def test_kernelsActiveDims(self):
''' Test sum and product compositional kernels '''
with self.test_context():
Q = 2 # latent dimensions
X_mean = gpflow.models.PCA_reduce(self.Y, Q)
kernsQuadratu = [
kernels.RBF(1, active_dims=[0]) + kernels.Linear(1, active_dims=[1]),
kernels.RBF(1, active_dims=[0]) + kernels.PeriodicKernel(1, active_dims=[1]),
kernels.RBF(1, active_dims=[0]) * kernels.Linear(1, active_dims=[1]),
kernels.RBF(Q)+kernels.Linear(Q)] # non-overlapping
kernsAnalytic = [
ekernels.Add([
ekernels.RBF(1, active_dims=[0]),
ekernels.Linear(1, active_dims=[1])]),
ekernels.Add([
ekernels.RBF(1, active_dims=[0]),
kernels.PeriodicKernel(1, active_dims=[1])]),
ekernels.Prod([
ekernels.RBF(1, active_dims=[0]),
ekernels.Linear(1, active_dims=[1])]),
ekernels.Add([
ekernels.RBF(Q),
ekernels.Linear(Q)])
]
fOnSeparateDims = [True, True, True, False]
Z = np.random.permutation(X_mean.copy())[:self.M]
# Also test default N(0,1) is used
X_prior_mean = np.zeros((self.N, Q))
X_prior_var = np.ones((self.N, Q))
Xtest = self.rng.randn(10, Q)
for kq, ka, sepDims in zip(kernsQuadratu, kernsAnalytic, fOnSeparateDims):
with self.test_context():
kq.num_gauss_hermite_points = 20 # speed up quadratic for tests
# RBF should throw error if quadrature is used
ka.kern_list[0].num_gauss_hermite_points = 0
if sepDims:
self.assertTrue(
ka.on_separate_dimensions,
'analytic kernel must not use quadrature')
mq = gpflow.models.BayesianGPLVM(
X_mean=X_mean,
X_var=np.ones((self.N, Q)),
Y=self.Y,
kern=kq,
M=self.M,
Z=Z,
X_prior_mean=X_prior_mean,
X_prior_var=X_prior_var)
ma = gpflow.models.BayesianGPLVM(
X_mean=X_mean,
X_var=np.ones((self.N, Q)),
Y=self.Y,
kern=ka,
M=self.M,
Z=Z)
ql = mq.compute_log_likelihood()
al = ma.compute_log_likelihood()
self.assertTrue(np.allclose(ql, al, atol=1e-2),
'Likelihood not equal %f<>%f' % (ql, al))
mu_f_a, var_f_a = ma.predict_f(Xtest)
mu_f_q, var_f_q = mq.predict_f(Xtest)
self.assertTrue(np.allclose(mu_f_a, mu_f_q, atol=1e-4),
('Posterior means different', mu_f_a-mu_f_q))
self.assertTrue(np.allclose(mu_f_a, mu_f_q, atol=1e-4),
('Posterior vars different', var_f_a-var_f_q))
if __name__ == "__main__":
tf.test.main()
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