Revision f4ce06708816199b1926b627322181b74d7a75eb authored by Alexander G. de G. Matthews on 30 August 2017, 11:28:47 UTC, committed by GitHub on 30 August 2017, 11:28:47 UTC
Release 0.4.0 message update
test_predict.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
from testing.gpflow_testcase import GPflowTestCase
class TestGaussian(GPflowTestCase):
def setUp(self):
with self.test_session():
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):
with self.test_session():
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):
with self.test_session():
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):
with self.test_session():
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(GPflowTestCase):
"""
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):
with self.test_session():
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):
with self.test_session():
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):
with self.test_session():
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)
with self.test_session():
self.model = gpflow.sgpr.SGPR(self.X, self.Y, Z=self.Z, kern=self.k())
class TestFullCovGPRFITC(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
with self.test_session():
self.model = gpflow.sgpr.GPRFITC(
self.X, self.Y,
Z=self.Z, kern=self.k())
class TestFullCovSVGP1(TestFullCov):
def setUp(self):
TestFullCov.setUp(self)
with self.test_session():
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)
with self.test_session():
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)
with self.test_session():
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)
with self.test_session():
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)
with self.test_session():
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)
with self.test_session():
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)
with self.test_session():
self.model = gpflow.sgpmc.SGPMC(
self.X, self.Y, kern=self.k(),
likelihood=gpflow.likelihoods.Gaussian(),
Z=self.Z)
if __name__ == "__main__":
unittest.main()
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