https://github.com/GPflow/GPflow
Tip revision: 0635a16e55a5604d5c3831271775e3031861f096 authored by alexggmatthews on 25 August 2016, 11:49:58 UTC
Changes to make model.py dump .json output not to be merged to master.
Changes to make model.py dump .json output not to be merged to master.
Tip revision: 0635a16
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_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)
l = GPflow.likelihoods.StudentT()
self.m1 = GPflow.gpmc.GPMC(X=X, Y=Y, kern=GPflow.kernels.Exponential(1), likelihood=l)
self.m2 = GPflow.sgpmc.SGPMC(X=X, Y=Y, kern=GPflow.kernels.Exponential(1), likelihood=l, 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()