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# Copyright 2016 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 initializers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import importlib
import numpy as np
from tensorflow.python.eager import backprop
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops.distributions import exponential as exponential_lib
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
def try_import(name): # pylint: disable=invalid-name
module = None
try:
module = importlib.import_module(name)
except ImportError as e:
tf_logging.warning("Could not import %s: %s" % (name, str(e)))
return module
stats = try_import("scipy.stats")
@test_util.run_all_in_graph_and_eager_modes
class ExponentialTest(test.TestCase):
def testExponentialLogPDF(self):
batch_size = 6
lam = constant_op.constant([2.0] * batch_size)
lam_v = 2.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
exponential = exponential_lib.Exponential(rate=lam)
log_pdf = exponential.log_prob(x)
self.assertEqual(log_pdf.get_shape(), (6,))
pdf = exponential.prob(x)
self.assertEqual(pdf.get_shape(), (6,))
if not stats:
return
expected_log_pdf = stats.expon.logpdf(x, scale=1 / lam_v)
self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf)
self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf))
def testExponentialCDF(self):
batch_size = 6
lam = constant_op.constant([2.0] * batch_size)
lam_v = 2.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
exponential = exponential_lib.Exponential(rate=lam)
cdf = exponential.cdf(x)
self.assertEqual(cdf.get_shape(), (6,))
if not stats:
return
expected_cdf = stats.expon.cdf(x, scale=1 / lam_v)
self.assertAllClose(self.evaluate(cdf), expected_cdf)
def testExponentialMean(self):
lam_v = np.array([1.0, 4.0, 2.5])
exponential = exponential_lib.Exponential(rate=lam_v)
self.assertEqual(exponential.mean().get_shape(), (3,))
if not stats:
return
expected_mean = stats.expon.mean(scale=1 / lam_v)
self.assertAllClose(self.evaluate(exponential.mean()), expected_mean)
def testExponentialVariance(self):
lam_v = np.array([1.0, 4.0, 2.5])
exponential = exponential_lib.Exponential(rate=lam_v)
self.assertEqual(exponential.variance().get_shape(), (3,))
if not stats:
return
expected_variance = stats.expon.var(scale=1 / lam_v)
self.assertAllClose(
self.evaluate(exponential.variance()), expected_variance)
def testExponentialEntropy(self):
lam_v = np.array([1.0, 4.0, 2.5])
exponential = exponential_lib.Exponential(rate=lam_v)
self.assertEqual(exponential.entropy().get_shape(), (3,))
if not stats:
return
expected_entropy = stats.expon.entropy(scale=1 / lam_v)
self.assertAllClose(self.evaluate(exponential.entropy()), expected_entropy)
def testExponentialSample(self):
lam = constant_op.constant([3.0, 4.0])
lam_v = [3.0, 4.0]
n = constant_op.constant(100000)
exponential = exponential_lib.Exponential(rate=lam)
samples = exponential.sample(n, seed=137)
sample_values = self.evaluate(samples)
self.assertEqual(sample_values.shape, (100000, 2))
self.assertFalse(np.any(sample_values < 0.0))
if not stats:
return
for i in range(2):
self.assertLess(
stats.kstest(sample_values[:, i],
stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01)
def testExponentialSampleMultiDimensional(self):
batch_size = 2
lam_v = [3.0, 22.0]
lam = constant_op.constant([lam_v] * batch_size)
exponential = exponential_lib.Exponential(rate=lam)
n = 100000
samples = exponential.sample(n, seed=138)
self.assertEqual(samples.get_shape(), (n, batch_size, 2))
sample_values = self.evaluate(samples)
self.assertFalse(np.any(sample_values < 0.0))
if not stats:
return
for i in range(2):
self.assertLess(
stats.kstest(sample_values[:, 0, i],
stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01)
self.assertLess(
stats.kstest(sample_values[:, 1, i],
stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01)
def testFullyReparameterized(self):
lam = constant_op.constant([0.1, 1.0])
with backprop.GradientTape() as tape:
tape.watch(lam)
exponential = exponential_lib.Exponential(rate=lam)
samples = exponential.sample(100)
grad_lam = tape.gradient(samples, lam)
self.assertIsNotNone(grad_lam)
def testExponentialWithSoftplusRate(self):
lam = [-2.2, -3.4]
exponential = exponential_lib.ExponentialWithSoftplusRate(rate=lam)
self.assertAllClose(
self.evaluate(nn_ops.softplus(lam)), self.evaluate(exponential.rate))
if __name__ == "__main__":
test.main()
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