# 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.
import tensorflow as tf
import numpy as np
from numpy.testing import assert_almost_equal
import gpflow
from gpflow.test_util import GPflowTestCase
class Quadratic(gpflow.models.Model):
def __init__(self):
rng = np.random.RandomState(0)
gpflow.models.Model.__init__(self)
self.x = gpflow.Param(rng.randn(10))
@gpflow.params_as_tensors
def _build_likelihood(self):
return tf.negative(tf.reduce_sum(tf.square(self.x)))
class TestOptimize(GPflowTestCase):
def test_adam(self):
with self.test_context():
m = Quadratic()
opt = gpflow.train.AdamOptimizer(0.01)
opt.minimize(m, maxiter=5000)
self.assertTrue(m.x.read_value().max() < 1e-2)
def test_lbfgsb(self):
with self.test_context():
m = Quadratic()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=1000)
self.assertTrue(m.x.read_value().max() < 1e-6)
class Empty(gpflow.models.Model):
def __init__(self, *args, **kwargs):
if 'name' not in kwargs:
kwargs['name'] = 'Empty'
super().__init__(*args, **kwargs)
def _build_likelihood(self):
return tf.convert_to_tensor(1., dtype=gpflow.settings.float_type)
class EmptyTest(GPflowTestCase):
def test_compile_model_without_parameters(self):
with self.test_context():
m = Empty()
assert_almost_equal(m.compute_log_likelihood(), 1.0)
assert_almost_equal(m.compute_log_prior(), 0.0)
def test_parameters_list_empty(self):
with self.test_context():
m = Empty(autobuild=False)
self.assertEqual(list(m.parameters), [])
self.assertEqual(list(m.trainable_parameters), [])
self.assertEqual(list(m.params), [])
m.compile()
self.assertEqual(list(m.parameters), [])
self.assertEqual(list(m.trainable_parameters), [])
self.assertEqual(list(m.params), [])
def test_objective_tensor(self):
with self.test_context():
m = Empty(autobuild=False)
self.assertEqual(m.objective, None)
m.build()
self.assertTrue(gpflow.misc.is_tensor(m.objective))
class ReplaceParameterTest(GPflowTestCase):
class Origin(gpflow.models.Model):
def __init__(self):
super(ReplaceParameterTest.Origin, self).__init__()
self.a = gpflow.Param(1.)
self.b = gpflow.Param(2.)
@gpflow.params_as_tensors
def _build_likelihood(self):
return tf.square(self.a) + tf.square(self.b)
def test_replace_parameter(self):
class OriginSuccess(ReplaceParameterTest.Origin):
def __init__(self):
super(OriginSuccess, self).__init__()
self.b = gpflow.Param(np.array(3.))
class OriginAllDataholders(ReplaceParameterTest.Origin):
def __init__(self):
super(OriginAllDataholders, self).__init__()
self.a = gpflow.DataHolder(np.array(2.))
self.b = gpflow.DataHolder(np.array(2.))
with self.test_context():
m0 = self.Origin()
m0.compile()
assert_almost_equal(m0.compute_log_likelihood(), 5.0)
m1 = OriginSuccess()
m1.compile()
assert_almost_equal(m1.compute_log_likelihood(), 10.0)
m2 = OriginAllDataholders()
m2.compile()
assert_almost_equal(m2.compute_log_likelihood(), 8.0)
class KeyboardRaiser:
"""
This wraps a function and makes it raise a KeyboardInterrupt after some number of calls
"""
def __init__(self, iters_to_raise):
self.iters_to_raise = iters_to_raise
self.count = 0
def __call__(self, *a, **kw):
self.count += 1
if self.count >= self.iters_to_raise:
raise KeyboardInterrupt
def setup_sgpr():
X = np.random.randn(1000, 3)
Y = np.random.randn(1000, 3)
Z = np.random.randn(100, 3)
return gpflow.models.SGPR(X, Y, Z=Z, kern=gpflow.kernels.RBF(3))
class TestLikelihoodAutoflow(GPflowTestCase):
def test_lik_and_prior(self):
with self.test_context(graph=tf.Graph()):
m = setup_sgpr()
l0 = m.compute_log_likelihood()
p0 = m.compute_log_prior()
m.clear()
with self.test_context(graph=tf.Graph()):
m.kern.variance.prior = gpflow.priors.Gamma(1.4, 1.6)
m.compile()
l1 = m.compute_log_likelihood()
p1 = m.compute_log_prior()
self.assertEqual(p0, 0.0)
self.assertNotEqual(p0, p1)
self.assertEqual(l0, l1)
class TestName(GPflowTestCase):
def test_name(self):
with self.test_context():
m1 = Empty()
self.assertEqual(m1.name, 'Empty')
m2 = Empty(name='foo')
self.assertEqual(m2.name, 'foo')
class EvalDataSVGP(gpflow.models.SVGP):
@gpflow.decors.autoflow()
@gpflow.decors.params_as_tensors
def XY(self):
return self.X, self.Y
class TestMinibatchSVGP(GPflowTestCase):
def test_minibatch_sync(self):
with self.test_context():
X = np.random.randn(1000, 1)
Y = X.copy()
Z = X[:100, :].copy()
size = 10
m = EvalDataSVGP(X, Y, gpflow.kernels.RBF(1),
gpflow.likelihoods.Gaussian(),
minibatch_size=size, Z=Z)
eX_prev, eY_prev = np.random.randn(size, 1), np.random.randn(size, 1)
for _ in range(10):
eX, eY = m.XY()
assert not np.allclose(eX, eX_prev)
assert not np.allclose(eY, eY_prev)
assert np.allclose(eX, eY)
eX_prev, eY_prev = eX, eY
# class TestNoRecompileThroughNewModelInstance(GPflowTestCase):
# """ Regression tests for Bug #454 """
# def setUp(self):
# self.X = np.random.rand(10, 2)
# self.Y = np.random.rand(10, 1)
# def test_gpr(self):
# with self.test_context():
# m1 = gpflow.models.GPR(self.X, self.Y, gpflow.kernels.Matern32(2))
# m1.compile()
# m2 = gpflow.models.GPR(self.X, self.Y, gpflow.kernels.Matern32(2))
# self.assertFalse(m1._needs_recompile)
# def test_sgpr(self):
# with self.test_context():
# m1 = gpflow.models.SGPR(self.X, self.Y, gpflow.kernels.Matern32(2), Z=self.X)
# m1.compile()
# m2 = gpflow.models.SGPR(self.X, self.Y, gpflow.kernels.Matern32(2), Z=self.X)
# self.assertFalse(m1._needs_recompile)
# def test_gpmc(self):
# with self.test_context():
# m1 = gpflow.models.GPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# m1.compile()
# m2 = gpflow.models.GPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# self.assertFalse(m1._needs_recompile)
# def test_sgpmc(self):
# with self.test_context():
# m1 = gpflow.models.SGPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# m1.compile()
# m2 = gpflow.models.SGPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# self.assertFalse(m1._needs_recompile)
# def test_svgp(self):
# with self.test_context():
# m1 = gpflow.models.SVGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# m1.compile()
# m2 = gpflow.models.SVGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# self.assertFalse(m1._needs_recompile)
# def test_vgp(self):
# with self.test_context():
# m1 = gpflow.models.VGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# m1.compile()
# m2 = gpflow.models.VGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# self.assertFalse(m1._needs_recompile)
if __name__ == "__main__":
tf.test.main()
