https://github.com/GPflow/GPflow
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_methods.py
import GPflow
import numpy as np
import unittest
import tensorflow as tf
class TestMethods(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
self.rng = np.random.RandomState(0)
self.X = self.rng.randn(100, 2)
self.Y = self.rng.randn(100, 1)
self.Z = self.rng.randn(10, 2)
self.lik = GPflow.likelihoods.Gaussian()
self.kern = GPflow.kernels.Matern32(2)
self.Xs = self.rng.randn(10, 2)
# make one of each model
self.ms = []
#for M in (GPflow.gpmc.GPMC, GPflow.vgp.VGP):
for M in (GPflow.vgp.VGP, GPflow.gpmc.GPMC):
self.ms.append(M(self.X, self.Y, self.kern, self.lik))
for M in (GPflow.sgpmc.SGPMC, GPflow.svgp.SVGP):
self.ms.append(M(self.X, self.Y, self.kern, self.lik, self.Z))
self.ms.append(GPflow.gpr.GPR(self.X, self.Y, self.kern))
self.ms.append(GPflow.sgpr.SGPR(self.X, self.Y, self.kern, Z=self.Z))
self.ms.append(GPflow.sgpr.GPRFITC(self.X, self.Y, self.kern, Z=self.Z))
def test_all(self):
# test sizes.
for m in self.ms:
m._compile()
f, g = m._objective(m.get_free_state())
self.assertTrue(f.size == 1)
self.assertTrue(g.size == m.get_free_state().size)
def test_tf_optimize(self):
for m in self.ms:
trainer = tf.train.AdamOptimizer(learning_rate=0.001)
if isinstance(m, (GPflow.gpr.GPR,GPflow.vgp.VGP,GPflow.svgp.SVGP)):
optimizeOp = m._compile(trainer)
self.assertTrue(optimizeOp is not None)
def test_predict_f(self):
for m in self.ms:
mf, vf = m.predict_f(self.Xs)
self.assertTrue(mf.shape == vf.shape)
self.assertTrue(mf.shape == (10, 1))
self.assertTrue(np.all(vf >= 0.0))
def test_predict_y(self):
for m in self.ms:
mf, vf = m.predict_y(self.Xs)
self.assertTrue(mf.shape == vf.shape)
self.assertTrue(mf.shape == (10, 1))
self.assertTrue(np.all(vf >= 0.0))
def test_predict_density(self):
self.Ys = self.rng.randn(10, 1)
for m in self.ms:
d = m.predict_density(self.Xs, self.Ys)
self.assertTrue(d.shape == (10, 1))
class TestSVGP(unittest.TestCase):
"""
The SVGP has four modes of operation. with and without whitening, with and
without diagonals.
Here we make sure thet the bound on the likelihood is the same when using
both representations (as far as possible)
"""
def setUp(self):
tf.reset_default_graph()
self.rng = np.random.RandomState(0)
self.X = self.rng.randn(20, 1)
self.Y = self.rng.randn(20, 2)
self.Z = self.rng.randn(3, 1)
def test_white(self):
m1 = GPflow.svgp.SVGP(self.X, self.Y,
kern=GPflow.kernels.RBF(1),
likelihood=GPflow.likelihoods.Exponential(),
Z=self.Z, q_diag=True, whiten=True)
m2 = GPflow.svgp.SVGP(self.X, self.Y,
kern=GPflow.kernels.RBF(1),
likelihood=GPflow.likelihoods.Exponential(),
Z=self.Z, q_diag=False, whiten=True)
m1._compile()
m2._compile()
qsqrt, qmean = self.rng.randn(2, 3, 2)
qsqrt = (qsqrt**2)*0.01
m1.q_sqrt = qsqrt
m1.q_mu = qmean
m2.q_sqrt = np.array([np.diag(qsqrt[:, 0]),
np.diag(qsqrt[:, 1])]).swapaxes(0, 2)
m2.q_mu = qmean
self.assertTrue(np.allclose(m1._objective(m1.get_free_state())[0],
m2._objective(m2.get_free_state())[0]))
def test_notwhite(self):
m1 = GPflow.svgp.SVGP(self.X,
self.Y,
kern=GPflow.kernels.RBF(1) +
GPflow.kernels.White(1),
likelihood=GPflow.likelihoods.Exponential(),
Z=self.Z,
q_diag=True,
whiten=False)
m2 = GPflow.svgp.SVGP(self.X,
self.Y,
kern=GPflow.kernels.RBF(1) +
GPflow.kernels.White(1),
likelihood=GPflow.likelihoods.Exponential(),
Z=self.Z,
q_diag=False,
whiten=False)
m1._compile()
m2._compile()
qsqrt, qmean = self.rng.randn(2, 3, 2)
qsqrt = (qsqrt**2)*0.01
m1.q_sqrt = qsqrt
m1.q_mu = qmean
m2.q_sqrt = np.array([np.diag(qsqrt[:, 0]), np.diag(qsqrt[:, 1])]).swapaxes(0, 2)
m2.q_mu = qmean
self.assertTrue(np.allclose(m1._objective(m1.get_free_state())[0],
m2._objective(m2.get_free_state())[0]))
def test_q_sqrt_fixing(self):
"""
In response to bug #46, we need to make sure that the q_sqrt matrix can be fixed
"""
m1 = GPflow.svgp.SVGP(self.X, self.Y,
kern=GPflow.kernels.RBF(1) + GPflow.kernels.White(1),
likelihood=GPflow.likelihoods.Exponential(),
Z=self.Z)
m1.q_sqrt.fixed = True
m1._compile()
class TestSparseMCMC(unittest.TestCase):
"""
This test makes sure that when the inducing points are the same as the data
points, the sparse mcmc is the same as full mcmc
"""
def setUp(self):
tf.reset_default_graph()
rng = np.random.RandomState(0)
X = rng.randn(10, 1)
Y = rng.randn(10, 1)
v_vals = rng.randn(10, 1)
lik = GPflow.likelihoods.StudentT
self.m1 = GPflow.gpmc.GPMC(X=X, Y=Y, kern=GPflow.kernels.Exponential(1), likelihood=lik())
self.m2 = GPflow.sgpmc.SGPMC(X=X, Y=Y, kern=GPflow.kernels.Exponential(1), likelihood=lik(), Z=X.copy())
self.m1.V = v_vals
self.m2.V = v_vals.copy()
self.m1.kern.lengthscale = .8
self.m2.kern.lengthscale = .8
self.m1.kern.variance = 4.2
self.m2.kern.variance = 4.2
self.m1._compile()
self.m2._compile()
def test_likelihoods_and_gradients(self):
f1, _ = self.m1._objective(self.m1.get_free_state())
f2, _ = self.m2._objective(self.m2.get_free_state())
self.assertTrue(np.allclose(f1, f2))
# the parameters might not be in the same order, so
# sort the gradients before checking they're the same
_, g1 = self.m1._objective(self.m1.get_free_state())
_, g2 = self.m2._objective(self.m2.get_free_state())
g1 = np.sort(g1)
g2 = np.sort(g2)
self.assertTrue(np.allclose(g1, g2, 1e-4))
if __name__ == "__main__":
unittest.main()