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# Copyright 2017 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.distributions.python.ops import deterministic as deterministic_lib
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
rng = np.random.RandomState(0)
class DeterministicTest(test.TestCase):
def testShape(self):
with self.test_session():
loc = rng.rand(2, 3, 4)
deterministic = deterministic_lib.Deterministic(loc)
self.assertAllEqual(deterministic.batch_shape_tensor().eval(), (2, 3, 4))
self.assertAllEqual(deterministic.batch_shape, (2, 3, 4))
self.assertAllEqual(deterministic.event_shape_tensor().eval(), [])
self.assertEqual(deterministic.event_shape, tensor_shape.TensorShape([]))
def testInvalidTolRaises(self):
loc = rng.rand(2, 3, 4).astype(np.float32)
deterministic = deterministic_lib.Deterministic(
loc, atol=-1, validate_args=True)
with self.test_session():
with self.assertRaisesOpError("Condition x >= 0"):
deterministic.prob(0.).eval()
def testProbWithNoBatchDimsIntegerType(self):
deterministic = deterministic_lib.Deterministic(0)
with self.test_session():
self.assertAllClose(1, deterministic.prob(0).eval())
self.assertAllClose(0, deterministic.prob(2).eval())
self.assertAllClose([1, 0], deterministic.prob([0, 2]).eval())
def testProbWithNoBatchDims(self):
deterministic = deterministic_lib.Deterministic(0.)
with self.test_session():
self.assertAllClose(1., deterministic.prob(0.).eval())
self.assertAllClose(0., deterministic.prob(2.).eval())
self.assertAllClose([1., 0.], deterministic.prob([0., 2.]).eval())
def testProbWithDefaultTol(self):
loc = [[0., 1.], [2., 3.]]
x = [[0., 1.1], [1.99, 3.]]
deterministic = deterministic_lib.Deterministic(loc)
expected_prob = [[1., 0.], [0., 1.]]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((2, 2), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbWithNonzeroATol(self):
loc = [[0., 1.], [2., 3.]]
x = [[0., 1.1], [1.99, 3.]]
deterministic = deterministic_lib.Deterministic(loc, atol=0.05)
expected_prob = [[1., 0.], [1., 1.]]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((2, 2), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbWithNonzeroATolIntegerType(self):
loc = [[0, 1], [2, 3]]
x = [[0, 2], [4, 2]]
deterministic = deterministic_lib.Deterministic(loc, atol=1)
expected_prob = [[1, 1], [0, 1]]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((2, 2), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbWithNonzeroRTol(self):
loc = [[0., 1.], [100., 100.]]
x = [[0., 1.1], [100.1, 103.]]
deterministic = deterministic_lib.Deterministic(loc, rtol=0.01)
expected_prob = [[1., 0.], [1., 0.]]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((2, 2), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbWithNonzeroRTolIntegerType(self):
loc = [[10, 10, 10], [10, 10, 10]]
x = [[10, 20, 30], [10, 20, 30]]
# Batch 0 will have rtol = 0
# Batch 1 will have rtol = 1 (100% slack allowed)
deterministic = deterministic_lib.Deterministic(loc, rtol=[[0], [1]])
expected_prob = [[1, 0, 0], [1, 1, 0]]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((2, 3), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testCdfWithDefaultTol(self):
loc = [[0., 0.], [0., 0.]]
x = [[-1., -0.1], [-0.01, 1.000001]]
deterministic = deterministic_lib.Deterministic(loc)
expected_cdf = [[0., 0.], [0., 1.]]
with self.test_session():
cdf = deterministic.cdf(x)
self.assertAllEqual((2, 2), cdf.get_shape())
self.assertAllEqual(expected_cdf, cdf.eval())
def testCdfWithNonzeroATol(self):
loc = [[0., 0.], [0., 0.]]
x = [[-1., -0.1], [-0.01, 1.000001]]
deterministic = deterministic_lib.Deterministic(loc, atol=0.05)
expected_cdf = [[0., 0.], [1., 1.]]
with self.test_session():
cdf = deterministic.cdf(x)
self.assertAllEqual((2, 2), cdf.get_shape())
self.assertAllEqual(expected_cdf, cdf.eval())
def testCdfWithNonzeroRTol(self):
loc = [[1., 1.], [100., 100.]]
x = [[0.9, 1.], [99.9, 97]]
deterministic = deterministic_lib.Deterministic(loc, rtol=0.01)
expected_cdf = [[0., 1.], [1., 0.]]
with self.test_session():
cdf = deterministic.cdf(x)
self.assertAllEqual((2, 2), cdf.get_shape())
self.assertAllEqual(expected_cdf, cdf.eval())
def testSampleNoBatchDims(self):
deterministic = deterministic_lib.Deterministic(0.)
for sample_shape in [(), (4,)]:
with self.test_session():
sample = deterministic.sample(sample_shape)
self.assertAllEqual(sample_shape, sample.get_shape())
self.assertAllClose(
np.zeros(sample_shape).astype(np.float32), sample.eval())
def testSampleWithBatchDims(self):
deterministic = deterministic_lib.Deterministic([0., 0.])
for sample_shape in [(), (4,)]:
with self.test_session():
sample = deterministic.sample(sample_shape)
self.assertAllEqual(sample_shape + (2,), sample.get_shape())
self.assertAllClose(
np.zeros(sample_shape + (2,)).astype(np.float32), sample.eval())
def testSampleDynamicWithBatchDims(self):
loc = array_ops.placeholder(np.float32)
sample_shape = array_ops.placeholder(np.int32)
deterministic = deterministic_lib.Deterministic(loc)
for sample_shape_ in [(), (4,)]:
with self.test_session():
sample_ = deterministic.sample(sample_shape).eval(
feed_dict={loc: [0., 0.],
sample_shape: sample_shape_})
self.assertAllClose(
np.zeros(sample_shape_ + (2,)).astype(np.float32), sample_)
class VectorDeterministicTest(test.TestCase):
def testShape(self):
with self.test_session():
loc = rng.rand(2, 3, 4)
deterministic = deterministic_lib.VectorDeterministic(loc)
self.assertAllEqual(deterministic.batch_shape_tensor().eval(), (2, 3))
self.assertAllEqual(deterministic.batch_shape, (2, 3))
self.assertAllEqual(deterministic.event_shape_tensor().eval(), [4])
self.assertEqual(deterministic.event_shape, tensor_shape.TensorShape([4]))
def testInvalidTolRaises(self):
loc = rng.rand(2, 3, 4).astype(np.float32)
deterministic = deterministic_lib.VectorDeterministic(
loc, atol=-1, validate_args=True)
with self.test_session():
with self.assertRaisesOpError("Condition x >= 0"):
deterministic.prob(loc).eval()
def testInvalidXRaises(self):
loc = rng.rand(2, 3, 4).astype(np.float32)
deterministic = deterministic_lib.VectorDeterministic(
loc, atol=-1, validate_args=True)
with self.test_session():
with self.assertRaisesRegexp(ValueError, "must have rank at least 1"):
deterministic.prob(0.).eval()
def testProbVectorDeterministicWithNoBatchDims(self):
# 0 batch of deterministics on R^1.
deterministic = deterministic_lib.VectorDeterministic([0.])
with self.test_session():
self.assertAllClose(1., deterministic.prob([0.]).eval())
self.assertAllClose(0., deterministic.prob([2.]).eval())
self.assertAllClose([1., 0.], deterministic.prob([[0.], [2.]]).eval())
def testProbWithDefaultTol(self):
# 3 batch of deterministics on R^2.
loc = [[0., 1.], [2., 3.], [4., 5.]]
x = [[0., 1.], [1.9, 3.], [3.99, 5.]]
deterministic = deterministic_lib.VectorDeterministic(loc)
expected_prob = [1., 0., 0.]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((3,), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbWithNonzeroATol(self):
# 3 batch of deterministics on R^2.
loc = [[0., 1.], [2., 3.], [4., 5.]]
x = [[0., 1.], [1.9, 3.], [3.99, 5.]]
deterministic = deterministic_lib.VectorDeterministic(loc, atol=0.05)
expected_prob = [1., 0., 1.]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((3,), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbWithNonzeroRTol(self):
# 3 batch of deterministics on R^2.
loc = [[0., 1.], [1., 1.], [100., 100.]]
x = [[0., 1.], [0.9, 1.], [99.9, 100.1]]
deterministic = deterministic_lib.VectorDeterministic(loc, rtol=0.01)
expected_prob = [1., 0., 1.]
with self.test_session():
prob = deterministic.prob(x)
self.assertAllEqual((3,), prob.get_shape())
self.assertAllEqual(expected_prob, prob.eval())
def testProbVectorDeterministicWithNoBatchDimsOnRZero(self):
# 0 batch of deterministics on R^0.
deterministic = deterministic_lib.VectorDeterministic(
[], validate_args=True)
with self.test_session():
self.assertAllClose(1., deterministic.prob([]).eval())
def testProbVectorDeterministicWithNoBatchDimsOnRZeroRaisesIfXNotInSameRk(
self):
# 0 batch of deterministics on R^0.
deterministic = deterministic_lib.VectorDeterministic(
[], validate_args=True)
with self.test_session():
with self.assertRaisesOpError("not defined in the same space"):
deterministic.prob([1.]).eval()
def testSampleNoBatchDims(self):
deterministic = deterministic_lib.VectorDeterministic([0.])
for sample_shape in [(), (4,)]:
with self.test_session():
sample = deterministic.sample(sample_shape)
self.assertAllEqual(sample_shape + (1,), sample.get_shape())
self.assertAllClose(
np.zeros(sample_shape + (1,)).astype(np.float32), sample.eval())
def testSampleWithBatchDims(self):
deterministic = deterministic_lib.VectorDeterministic([[0.], [0.]])
for sample_shape in [(), (4,)]:
with self.test_session():
sample = deterministic.sample(sample_shape)
self.assertAllEqual(sample_shape + (2, 1), sample.get_shape())
self.assertAllClose(
np.zeros(sample_shape + (2, 1)).astype(np.float32), sample.eval())
def testSampleDynamicWithBatchDims(self):
loc = array_ops.placeholder(np.float32)
sample_shape = array_ops.placeholder(np.int32)
deterministic = deterministic_lib.VectorDeterministic(loc)
for sample_shape_ in [(), (4,)]:
with self.test_session():
sample_ = deterministic.sample(sample_shape).eval(
feed_dict={loc: [[0.], [0.]],
sample_shape: sample_shape_})
self.assertAllClose(
np.zeros(sample_shape_ + (2, 1)).astype(np.float32), sample_)
if __name__ == "__main__":
test.main()
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