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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 tensorflow.ops.random_ops.random_poisson."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
class RandomPoissonTest(test.TestCase):
"""This is a large test due to the moments computation taking some time."""
def _Sampler(self, num, lam, dtype, use_gpu, seed=None):
def func():
with self.test_session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
rng = random_ops.random_poisson(lam, [num], dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = sess.run(rng)
return ret
return func
# TODO(srvasude): Factor this out along with the corresponding moment testing
# method in random_gamma_test into a single library.
def testMoments(self):
try:
from scipy import stats # pylint: disable=g-import-not-at-top
except ImportError as e:
tf_logging.warn("Cannot test moments: %s", e)
return
# The moments test is a z-value test. This is the largest z-value
# we want to tolerate. Since the z-test approximates a unit normal
# distribution, it should almost definitely never exceed 6.
z_limit = 6.0
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
# Test when lam < 10 and when lam >= 10
for stride in 0, 4, 10:
for lam in (3., 20):
max_moment = 5
sampler = self._Sampler(10000, lam, dt, use_gpu=False, seed=12345)
moments = [0] * (max_moment + 1)
moments_sample_count = [0] * (max_moment + 1)
x = np.array(sampler().flat) # sampler does 10x samples
for k in range(len(x)):
moment = 1.
for i in range(max_moment + 1):
index = k + i * stride
if index >= len(x):
break
moments[i] += moment
moments_sample_count[i] += 1
moment *= x[index]
for i in range(max_moment + 1):
moments[i] /= moments_sample_count[i]
for i in range(1, max_moment + 1):
g = stats.poisson(lam)
if stride == 0:
moments_i_mean = g.moment(i)
moments_i_squared = g.moment(2 * i)
else:
moments_i_mean = pow(g.moment(1), i)
moments_i_squared = pow(g.moment(2), i)
moments_i_var = (
moments_i_squared - moments_i_mean * moments_i_mean)
# Assume every operation has a small numerical error.
# It takes i multiplications to calculate one i-th moment.
error_per_moment = i * 1e-6
total_variance = (
moments_i_var / moments_sample_count[i] + error_per_moment)
if not total_variance:
total_variance = 1e-10
# z_test is approximately a unit normal distribution.
z_test = abs(
(moments[i] - moments_i_mean) / np.sqrt(total_variance))
self.assertLess(z_test, z_limit)
# Checks that the CPU and GPU implementation returns the same results,
# given the same random seed
def testCPUGPUMatch(self):
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
results = {}
for use_gpu in [False, True]:
sampler = self._Sampler(1000, 1.0, dt, use_gpu=use_gpu, seed=12345)
results[use_gpu] = sampler()
if dt == dtypes.float16:
self.assertAllClose(results[False], results[True], rtol=1e-3, atol=1e-3)
else:
self.assertAllClose(results[False], results[True], rtol=1e-6, atol=1e-6)
def testSeed(self):
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
sx = self._Sampler(1000, 1.0, dt, use_gpu=True, seed=345)
sy = self._Sampler(1000, 1.0, dt, use_gpu=True, seed=345)
self.assertAllEqual(sx(), sy())
def testNoCSE(self):
"""CSE = constant subexpression eliminator.
SetIsStateful() should prevent two identical random ops from getting
merged.
"""
for dtype in dtypes.float16, dtypes.float32, dtypes.float64:
with self.test_session(use_gpu=True):
rnd1 = random_ops.random_poisson(2.0, [24], dtype=dtype)
rnd2 = random_ops.random_poisson(2.0, [24], dtype=dtype)
diff = rnd2 - rnd1
# Since these are all positive integers, the norm will
# be at least 1 if they are different.
self.assertGreaterEqual(np.linalg.norm(diff.eval()), 1)
def testShape(self):
# Fully known shape.
rnd = random_ops.random_poisson(2.0, [150], seed=12345)
self.assertEqual([150], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([1, 2, 3]),
shape=[150],
seed=12345)
self.assertEqual([150, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([1, 2, 3]),
shape=[20, 30],
seed=12345)
self.assertEqual([20, 30, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32, shape=(2,)),
shape=[12],
seed=12345)
self.assertEqual([12, 2], rnd.get_shape().as_list())
# Partially known shape.
rnd = random_ops.random_poisson(
lam=array_ops.ones([7, 3]),
shape=array_ops.placeholder(dtypes.int32, shape=(1,)),
seed=12345)
self.assertEqual([None, 7, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([9, 6]),
shape=array_ops.placeholder(dtypes.int32, shape=(3,)),
seed=12345)
self.assertEqual([None, None, None, 9, 6], rnd.get_shape().as_list())
# Unknown shape.
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32),
shape=array_ops.placeholder(dtypes.int32),
seed=12345)
self.assertIs(None, rnd.get_shape().ndims)
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32),
shape=[50],
seed=12345)
self.assertIs(None, rnd.get_shape().ndims)
if __name__ == "__main__":
test.main()
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