swh:1:snp:d2bcff616bbf538fe8ce2a9c384200307730292a
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_predict.py
import GPflow
import numpy as np
import unittest
import tensorflow as tf
class TestGaussian(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.kern = GPflow.kernels.Matern32(2) + GPflow.kernels.White(1)
self.Xtest = self.rng.randn(10, 2)
self.Ytest = self.rng.randn(10, 1)
# make a Gaussian model
self.m = GPflow.gpr.GPR(self.X, self.Y, kern=self.kern)
def test_all(self):
mu_f, var_f = self.m.predict_f(self.Xtest)
mu_y, var_y = self.m.predict_y(self.Xtest)
self.assertTrue(np.allclose(mu_f, mu_y))
self.assertTrue(np.allclose(var_f, var_y - 1.))
def test_density(self):
mu_y, var_y = self.m.predict_y(self.Xtest)
density = self.m.predict_density(self.Xtest, self.Ytest)
density_hand = -0.5*np.log(2*np.pi) - 0.5*np.log(var_y) - 0.5*np.square(mu_y - self.Ytest)/var_y
self.assertTrue(np.allclose(density_hand, density))
def test_recompile(self):
mu_f, var_f = self.m.predict_f(self.Xtest)
mu_y, var_y = self.m.predict_y(self.Xtest)
density = self.m.predict_density(self.Xtest, self.Ytest)
#change a fix and see if these things still compile
self.m.likelihood.variance = 0.2
self.m.likelihood.variance.fixed = True
#this will fail unless a recompile has been triggered
mu_f, var_f = self.m.predict_f(self.Xtest)
mu_y, var_y = self.m.predict_y(self.Xtest)
density = self.m.predict_density(self.Xtest, self.Ytest)
class TestFullCov(unittest.TestCase):
"""
this base class requires inherriting to specify the model.
This test structure is more complex that, say, looping over the models, but
makses all the tests much smaller and so less prone to erroring out. Also,
if a test fails, it should be clearer where the error is.
"""
def setUp(self):
tf.reset_default_graph()
self.input_dim = 3
self.output_dim = 2
self.N = 20
self.Ntest = 30
self.M = 5
rng = np.random.RandomState(0)
self.num_samples = 5
self.samples_shape = (self.num_samples, self.Ntest, self.output_dim)
self.covar_shape = (self.Ntest, self.Ntest, self.output_dim)
self.X, self.Y, self.Z, self.Xtest = rng.randn(self.N, self.input_dim),\
rng.randn(self.N, self.output_dim),\
rng.randn(self.M, self.input_dim),\
rng.randn(self.Ntest, self.input_dim)
self.k = lambda: GPflow.kernels.Matern32(self.input_dim)
self.model = GPflow.gpr.GPR(self.X, self.Y, kern=self.k())
def test_cov(self):
mu1, var = self.model.predict_f(self.Xtest)
mu2, covar = self.model.predict_f_full_cov(self.Xtest)
self.assertTrue(np.all(mu1 == mu2))
self.assertTrue(covar.shape == self.covar_shape)
self.assertTrue(var.shape == (self.Ntest, self.output_dim))
for i in range(self.output_dim):
self.assertTrue(np.allclose(var[:, i], np.diag(covar[:, :, i])))
def test_samples(self):
samples = self.model.predict_f_samples(self.Xtest, self.num_samples)
self.assertTrue(samples.shape == self.samples_shape)
class TestFullCovSGPR(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.sgpr.SGPR(self.X, self.Y, Z=self.Z, kern=self.k())
class TestFullCovGPRFITC(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.sgpr.GPRFITC(self.X, self.Y,
Z=self.Z, kern=self.k())
class TestFullCovSVGP1(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.svgp.SVGP(self.X, self.Y, Z=self.Z, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian(),
whiten=False, q_diag=True)
class TestFullCovSVGP2(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.svgp.SVGP(self.X, self.Y, Z=self.Z, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian(),
whiten=True, q_diag=False)
class TestFullCovSVGP3(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.svgp.SVGP(self.X, self.Y, Z=self.Z, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian(),
whiten=True, q_diag=True)
class TestFullCovSVGP4(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.svgp.SVGP(self.X, self.Y, Z=self.Z, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian(),
whiten=True, q_diag=False)
class TestFullCovVGP(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.vgp.VGP(self.X, self.Y, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian())
class TestFullCovGPMC(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.gpmc.GPMC(self.X, self.Y, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian())
class TestFullCovSGPMC(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
self.model = GPflow.sgpmc.SGPMC(self.X, self.Y, kern=self.k(),
likelihood=GPflow.likelihoods.Gaussian(),
Z=self.Z)
if __name__ == "__main__":
unittest.main()