# Copyright 2017 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, assert_allclose
import gpflow
from gpflow.test_util import GPflowTestCase
class SampleGaussianTest(GPflowTestCase):
class Gauss(gpflow.models.Model):
def __init__(self, **kwargs):
super(SampleGaussianTest.Gauss, self).__init__(**kwargs)
self.x = gpflow.Param(np.zeros(3))
@gpflow.params_as_tensors
def build_objective(self):
return 0.5 * tf.reduce_sum(tf.square(self.x))
def _build_likelihood(self):
return tf.constant(0.0, dtype=gpflow.settings.float_type)
@gpflow.defer_build()
def setUp(self):
with self.test_context():
tf.set_random_seed(1)
self.m = SampleGaussianTest.Gauss()
self.hmc = gpflow.train.HMC()
def test_mean_cov(self):
with self.test_context():
self.m.compile()
num_samples = 1000
samples = self.hmc.sample(self.m, num_samples=num_samples,
lmin=10, lmax=21, epsilon=0.05)
self.assertEqual(samples.shape, (num_samples, 2))
xs = np.array(samples[self.m.x.pathname].tolist(), dtype=np.float32)
mean = xs.mean(0)
cov = np.cov(xs.T)
cov_standard = np.eye(cov.shape[0])
# TODO(@awav): inspite of the fact that we set up graph's random seed,
# the operation seed is still assigned by tensorflow automatically
# and hence sample output numbers are not deterministic.
#
# self.assertTrue(np.sum(np.abs(mean) < 0.1) >= mean.size/2)
# assert_allclose(cov, cov_standard, rtol=1e-1, atol=1e-1)
def test_rng(self):
"""
Make sure all randomness can be atributed to the rng
"""
def get_samples():
num_samples = 100
m = SampleGaussianTest.Gauss()
m.compile()
hmc = gpflow.train.HMC()
samples = hmc.sample(m, num_samples=num_samples, epsilon=0.05,
lmin=10, lmax=21, thin=10)
return np.array(samples[m.x.pathname].values.tolist(), dtype=np.float32)
with self.test_context():
tf.set_random_seed(1)
s1 = get_samples()
with self.test_context():
tf.set_random_seed(2)
s2 = get_samples()
with self.test_context():
tf.set_random_seed(3)
s3 = get_samples()
self.assertFalse(np.all(s1 == s2))
self.assertFalse(np.all(s1 == s3))
def test_burn(self):
with self.test_context():
self.m.compile()
num_samples = 10
x0 = list(self.m.read_trainables().values())[0].copy()
samples = self.hmc.sample(self.m, num_samples=num_samples,
lmin=10, lmax=21, epsilon=0.05,
burn=10, logprobs=False)
x = samples.iloc[-1][0]
self.assertEqual(samples.shape, (10, 1))
self.assertEqual(x.shape, (3,))
self.assertFalse(np.all(x == x0))
def test_columns_names(self):
with self.test_session():
self.m.compile()
num_samples = 10
samples = self.hmc.sample(self.m, num_samples=num_samples,
lmin=10, lmax=21, epsilon=0.05)
names = [p.pathname for p in self.m.parameters]
names.append('logprobs')
names = set(names)
col_names = set(samples.columns)
self.assertEqual(col_names, names)
class Quadratic(gpflow.models.Model):
def __init__(self):
super(Quadratic, self).__init__()
rng = np.random.RandomState(0)
self.x = gpflow.Param(rng.randn(2), dtype=gpflow.settings.float_type)
@gpflow.params_as_tensors
def _build_likelihood(self):
return -tf.reduce_sum(tf.square(self.x))
class SampleModelTest(GPflowTestCase):
"""
Create a very simple model and make sure samples form is make sense.
"""
def setUp(self):
tf.set_random_seed(1)
def test_mean(self):
with self.test_context():
m = Quadratic()
hmc = gpflow.train.HMC()
num_samples = 400
samples = hmc.sample(m, num_samples=num_samples,
epsilon=0.05, lmin=10, lmax=20, thin=10)
xs = np.array(samples[m.x.pathname].tolist(), dtype=np.float32)
self.assertEqual(samples.shape, (400, 2))
self.assertEqual(xs.shape, (400, 2))
assert_almost_equal(xs.mean(0), np.zeros(2), decimal=1)
llh = [m.compute_log_likelihood(feed_dict=m.sample_feed_dict(s))
for i, s in samples.iterrows()]
assert_allclose(llh, - (xs**2).sum(1), atol=1e-6)
class CheckTrainingVariableState(GPflowTestCase):
def model(self):
X, Y = np.random.randn(2, 10, 1)
return gpflow.models.GPMC(
X, Y,
kern=gpflow.kernels.Matern32(1),
likelihood=gpflow.likelihoods.StudentT())
def test_last_update(self):
with self.test_context():
m = self.model()
hmc = gpflow.train.HMC()
samples = hmc.sample(m, num_samples=10, lmin=1, lmax=10, epsilon=0.05, thin=10)
self.check_last_variables_state(m, samples)
def test_with_fixed(self):
with self.test_context():
m = self.model()
m.kern.lengthscales.trainable = False
hmc = gpflow.train.HMC()
samples = hmc.sample(m, num_samples=10, lmax=10, epsilon=0.05)
missing_param = m.kern.lengthscales.pathname
self.assertTrue(missing_param not in samples)
self.check_last_variables_state(m, samples)
def test_multiple_runs(self):
with self.test_context():
m = self.model()
hmc = gpflow.train.HMC()
for n in [1, 2]:
samples = hmc.sample(m, num_samples=n, lmax=10, epsilon=0.05)
self.check_last_variables_state(m, samples)
def check_last_variables_state(self, m, samples):
xs = samples.drop('logprobs', axis=1)
params = {p.pathname: p for p in m.trainable_parameters}
self.assertEqual(set(params.keys()), set(xs.columns))
last = xs.iloc[-1]
for col in last.index:
assert_almost_equal(last[col], params[col].read_value())
if __name__ == '__main__':
tf.test.main()
