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Refactor out moment-testing and make more numpy friendly.

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PiperOrigin-RevId: 230804842
This commit is contained in:
A. Unique TensorFlower 2019-01-24 15:45:54 -08:00 committed by TensorFlower Gardener
parent 2e17f8b3b7
commit 7c929bf6ef
5 changed files with 100 additions and 89 deletions
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10
tensorflow/python/kernel_tests/random/BUILD
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@ -14,6 +14,14 @@ load("//tensorflow:tensorflow.bzl", "sycl_py_test")
# Please avoid the py_tests and cuda_py_tests (plural) while we
# fix the shared/overbroad dependencies.
py_library(
name = "util",
srcs = ["util.py"],
deps = [
"//third_party/py/numpy",
],
)
tf_py_test(
name = "random_shuffle_queue_test",
size = "small",
@ -115,6 +123,7 @@ cuda_py_test(
size = "medium",
srcs = ["random_gamma_test.py"],
additional_deps = [
":util",
"//third_party/py/numpy",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",
@ -151,6 +160,7 @@ cuda_py_test(
size = "medium",
srcs = ["random_poisson_test.py"],
additional_deps = [
":util",
"//third_party/py/numpy",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",

60
tensorflow/python/kernel_tests/random/random_gamma_test.py
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@ -18,8 +18,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
@ -27,6 +25,7 @@ from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import test_util
from tensorflow.python.kernel_tests.random import util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
@ -69,16 +68,6 @@ class RandomGammaTest(test.TestCase):
tf_logging.warn("Cannot test moments: %s" % e)
return
# Check the given array of samples matches the given theoretical moment
# function at different orders. The test is considered passing if the
# z-tests of all statistical moments are all below z_limit.
# Parameters:
# max_moments: the largest moments of the distribution to be tested
# stride: the distance between samples to check for statistical properties
# 0 means the n-th moment of each sample
# any other strides tests for spatial correlation between samples;
# z_limit: the maximum z-test we would consider the test to pass;
# 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.
@ -94,46 +83,13 @@ class RandomGammaTest(test.TestCase):
max_moment = min(6, scale // 2)
sampler = self._Sampler(
20000, alpha, 1 / scale, 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.gamma(alpha, scale=scale)
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)
# Calculate moment variance safely:
# This is just
# (moments_i_squared - moments_i_mean**2) / moments_sample_count[i]
normalized_moments_i_var = (
moments_i_mean / moments_sample_count[i] *
(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 * np.finfo(dt.as_numpy_dtype).eps
total_variance = (normalized_moments_i_var + error_per_moment)
tiny = np.finfo(dt.as_numpy_dtype).tiny
self.assertGreaterEqual(total_variance, 0)
if total_variance < tiny:
total_variance = tiny
# z_test is approximately a unit normal distribution.
z_test = abs(
(moments[i] - moments_i_mean) / math.sqrt(total_variance))
self.assertLess(z_test, z_limit)
z_scores = util.test_moment_matching(
sampler(),
max_moment,
stats.gamma(alpha, scale=scale),
stride=stride,
)
self.assertAllLess(z_scores, z_limit)
def _testZeroDensity(self, alpha):
"""Zero isn't in the support of the gamma distribution.

46
tensorflow/python/kernel_tests/random/random_poisson_test.py
View File

@ -24,6 +24,7 @@ from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.kernel_tests.random import util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
@ -49,14 +50,13 @@ class RandomPoissonTest(test.TestCase):
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.
@ -67,41 +67,13 @@ class RandomPoissonTest(test.TestCase):
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)
z_scores = util.test_moment_matching(
sampler(),
max_moment,
stats.poisson(lam),
stride=stride,
)
self.assertAllLess(z_scores, z_limit)
# Checks that the CPU and GPU implementation returns the same results,
# given the same random seed

72
tensorflow/python/kernel_tests/random/util.py Normal file
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@ -0,0 +1,72 @@
# 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.
# ==============================================================================
"""Utilities for testing random variables."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def test_moment_matching(
samples,
number_moments,
dist,
stride=0):
"""Return z-test scores for sample moments to match analytic moments.
Given `samples`, check that the first sample `number_moments` match
the given `dist` moments by doing a z-test.
Args:
samples: Samples from target distribution.
number_moments: Python `int` describing how many sample moments to check.
dist: SciPy distribution object that provides analytic moments.
stride: Distance between samples to check for statistical properties.
A stride of 0 means to use all samples, while other strides test for
spatial correlation.
Returns:
Array of z_test scores.
"""
sample_moments = []
expected_moments = []
variance_sample_moments = []
x = samples.flat
for i in range(1, number_moments + 1):
strided_range = x[::(i - 1) * stride + 1]
sample_moments.append(np.mean(strided_range ** i))
expected_moments.append(dist.moment(i))
variance_sample_moments.append(
(dist.moment(2 * i) - dist.moment(i) ** 2) / len(strided_range))
z_test_scores = []
for i in range(1, number_moments + 1):
# Assume every operation has a small numerical error.
# It takes i multiplications to calculate one i-th moment.
total_variance = (
variance_sample_moments[i - 1] +
i * np.finfo(samples.dtype).eps)
tiny = np.finfo(samples.dtype).tiny
assert np.all(total_variance > 0)
if total_variance < tiny:
total_variance = tiny
# z_test is approximately a unit normal distribution.
z_test_scores.append(abs(
(sample_moments[i - 1] - expected_moments[i - 1]) / np.sqrt(
total_variance)))
return z_test_scores

1
tensorflow/tools/pip_package/BUILD
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@ -82,6 +82,7 @@ COMMON_PIP_DEPS = [
"//tensorflow/python/data/kernel_tests:test_base",
"//tensorflow/python/debug:debug_pip",
"//tensorflow/python/eager:eager_pip",
"//tensorflow/python/kernel_tests/random:util",
"//tensorflow/python/kernel_tests/signal:test_util",
"//tensorflow/python/kernel_tests/testdata:self_adjoint_eig_op_test_files",
"//tensorflow/python/ops/ragged:ragged_test_util",