Revision 459d328f3191663c4a50bfef4e16fdb4651bd0c9 authored by Artem Artemev on 11 November 2017, 10:48:03 UTC, committed by Artem Artemev on 11 November 2017, 10:48:03 UTC
1 parent 601aee2
test_transforms.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 warnings
import tensorflow as tf
import numpy as np
from numpy.testing import assert_allclose
import gpflow
from gpflow.test_util import GPflowTestCase
from gpflow import settings
class TransformTests(GPflowTestCase):
def prepare(self):
x = tf.placeholder(settings.np_float, 10)
x_np = np.random.randn(10).astype(settings.np_float)
transforms = []
for transform in gpflow.transforms.Transform.__subclasses__():
if transform != gpflow.transforms.LowerTriangular:
transforms.append(transform())
else:
transforms.append(transform(4))
#self.transforms = [C() for C in gpflow.transforms.Transform.__subclasses__()]
transforms.append(gpflow.transforms.Logistic(7.3, 19.4))
return x, x_np, transforms
def test_tf_np_forward(self):
"""
Make sure the np forward transforms are the same as the tensorflow ones
"""
with self.test_context() as sess:
x, x_np, transforms = self.prepare()
ys = [t.forward_tensor(x) for t in transforms]
ys_tf = [sess.run(y, feed_dict={x: x_np}) for y in ys]
ys_np = [t.forward(x_np) for t in transforms]
for y1, y2 in zip(ys_tf, ys_np):
assert_allclose(y1, y2)
def test_forward_backward(self):
with self.test_context():
x, x_np, transforms = self.prepare()
ys_np = [t.forward(x_np) for t in transforms]
xs_np = [t.backward(y) for t, y in zip(transforms, ys_np)]
for _t, x, _y in zip(transforms, xs_np, ys_np):
assert_allclose(x.reshape(x_np.shape), x_np)
# TODO(@awav): CHECK IT
# self.assertTrue(t.free_state_size(y.shape) == len(x))
def test_logjac(self):
"""
We have hand-crafted the log-jacobians for speed. Check they're correct
wrt a tensorflow derived version
"""
with self.test_context() as sess:
x, x_np, transforms = self.prepare()
# there is no jacobian: loop manually
def jacobian(f):
return tf.stack([tf.gradients(f(x)[i], x)[0] for i in range(10)])
tf_jacs = [tf.log(tf.matrix_determinant(jacobian(t.forward_tensor)))
for t in transforms
if not isinstance(t, gpflow.transforms.LowerTriangular)]
hand_jacs = [t.log_jacobian_tensor(x)
for t in transforms
if not isinstance(t, gpflow.transforms.LowerTriangular)]
for j1, j2 in zip(tf_jacs, hand_jacs):
j1_res = sess.run(j1, feed_dict={x: x_np})
j2_res = sess.run(j2, feed_dict={x: x_np})
assert_allclose(j1_res, j2_res)
class TestOverflow(GPflowTestCase):
"""
Bug #302 identified an overflow in the standard positive transform. This is a regression test.
"""
def setUp(self):
self.t = gpflow.transforms.Log1pe()
def testOverflow(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
y = self.t.forward(np.array([-1000, -300, -10, 10, 300, 1000]))
self.assertEqual(len(w), 0)
self.assertFalse(np.any(np.isinf(y)))
self.assertFalse(np.any(np.isnan(y)))
def testDivByZero(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
y = self.t.backward(np.array([self.t._lower]))
self.assertTrue(len(w) == 0)
self.assertFalse(np.any(np.isinf(y)))
self.assertFalse(np.any(np.isnan(y)))
class TestLowerTriTransform(GPflowTestCase):
"""
Some extra tests for the LowerTriangle transformation.
"""
def testErrors(self):
t = gpflow.transforms.LowerTriangular(1, 3)
t.forward(np.ones(3 * 6))
with self.assertRaises(ValueError):
t.forward(np.ones(3 * 7))
class TestDiagMatrixTransform(GPflowTestCase):
def setUp(self):
self.t1 = gpflow.transforms.DiagMatrix(dim=1)
self.t2 = gpflow.transforms.DiagMatrix(dim=3)
def test_forward_backward(self):
free_1d = np.random.randn(8)
fwd1d = self.t1.forward(free_1d)
assert_allclose(fwd1d.shape, np.array([len(free_1d), self.t1.dim, self.t1.dim]))
assert_allclose(free_1d, self.t1.backward(fwd1d))
size2d = 1
free_2d = np.random.randn(size2d, self.t2.dim).flatten()
fwd2d = self.t2.forward(free_2d)
assert_allclose(fwd2d.shape, np.array([size2d, self.t2.dim, self.t2.dim]))
assert_allclose(free_2d, self.t2.backward(fwd2d))
def test_tf_np_forward(self):
"""
Make sure the np forward transforms are the same as the tensorflow ones
"""
with self.test_context() as sess:
free = np.random.randn(8, self.t2.dim).flatten()
x = tf.placeholder(settings.np_float)
ys = sess.run(self.t2.forward_tensor(x), feed_dict={x: free})
assert_allclose(ys, self.t2.forward(free))
free = np.random.randn(1, self.t1.dim).flatten()
x = tf.placeholder(settings.np_float)
ys = sess.run(self.t1.forward_tensor(x), feed_dict={x: free})
assert_allclose(ys, self.t1.forward(free))
if __name__ == "__main__":
tf.test.main()
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