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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for Bijector."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.distributions.python.ops.bijectors.ordered import Ordered
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
from tensorflow.python.platform import test
class OrderedBijectorTest(test.TestCase):
"""Tests correctness of the ordered transformation."""
def setUp(self):
self._rng = np.random.RandomState(42)
@test_util.run_in_graph_and_eager_modes
def testBijectorVector(self):
with self.cached_session():
ordered = Ordered()
self.assertEqual("ordered", ordered.name)
x = np.asarray([[2., 3, 4], [4., 8, 13]])
y = [[2., 0, 0], [4., np.log(4.), np.log(5.)]]
self.assertAllClose(y, self.evaluate(ordered.forward(x)))
self.assertAllClose(x, self.evaluate(ordered.inverse(y)))
self.assertAllClose(
np.sum(np.asarray(y)[..., 1:], axis=-1),
self.evaluate(ordered.inverse_log_det_jacobian(y, event_ndims=1)),
atol=0.,
rtol=1e-7)
self.assertAllClose(
self.evaluate(-ordered.inverse_log_det_jacobian(y, event_ndims=1)),
self.evaluate(ordered.forward_log_det_jacobian(x, event_ndims=1)),
atol=0.,
rtol=1e-7)
def testBijectorUnknownShape(self):
with self.cached_session():
ordered = Ordered()
self.assertEqual("ordered", ordered.name)
x = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32)
real_x = np.asarray([[2., 3, 4], [4., 8, 13]])
y = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32)
real_y = [[2., 0, 0], [4., np.log(4.), np.log(5.)]]
self.assertAllClose(real_y, ordered.forward(x).eval(
feed_dict={x: real_x}))
self.assertAllClose(real_x, ordered.inverse(y).eval(
feed_dict={y: real_y}))
self.assertAllClose(
np.sum(np.asarray(real_y)[..., 1:], axis=-1),
ordered.inverse_log_det_jacobian(y, event_ndims=1).eval(
feed_dict={y: real_y}),
atol=0.,
rtol=1e-7)
self.assertAllClose(
-ordered.inverse_log_det_jacobian(y, event_ndims=1).eval(
feed_dict={y: real_y}),
ordered.forward_log_det_jacobian(x, event_ndims=1).eval(
feed_dict={x: real_x}),
atol=0.,
rtol=1e-7)
@test_util.run_in_graph_and_eager_modes
def testShapeGetters(self):
with self.cached_session():
x = tensor_shape.TensorShape([4])
y = tensor_shape.TensorShape([4])
bijector = Ordered(validate_args=True)
self.assertAllEqual(y, bijector.forward_event_shape(x))
self.assertAllEqual(y.as_list(),
self.evaluate(bijector.forward_event_shape_tensor(
x.as_list())))
self.assertAllEqual(x, bijector.inverse_event_shape(y))
self.assertAllEqual(x.as_list(),
self.evaluate(bijector.inverse_event_shape_tensor(
y.as_list())))
def testBijectiveAndFinite(self):
with self.cached_session():
ordered = Ordered()
x = np.sort(self._rng.randn(3, 10), axis=-1).astype(np.float32)
y = (self._rng.randn(3, 10)).astype(np.float32)
assert_bijective_and_finite(ordered, x, y, event_ndims=1)
if __name__ == "__main__":
test.main()
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