135 lines
4.9 KiB
Python
135 lines
4.9 KiB
Python
# Copyright 2018, The TensorFlow Authors.
|
|
#
|
|
# 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 GaussianAverageQuery."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from absl.testing import parameterized
|
|
import numpy as np
|
|
from six.moves import xrange
|
|
import tensorflow as tf
|
|
|
|
from privacy.optimizers import gaussian_query
|
|
from privacy.optimizers import test_utils
|
|
|
|
|
|
class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
|
|
|
|
def test_gaussian_sum_no_clip_no_noise(self):
|
|
with self.cached_session() as sess:
|
|
record1 = tf.constant([2.0, 0.0])
|
|
record2 = tf.constant([-1.0, 1.0])
|
|
|
|
query = gaussian_query.GaussianSumQuery(
|
|
l2_norm_clip=10.0, stddev=0.0)
|
|
query_result, _ = test_utils.run_query(query, [record1, record2])
|
|
result = sess.run(query_result)
|
|
expected = [1.0, 1.0]
|
|
self.assertAllClose(result, expected)
|
|
|
|
def test_gaussian_sum_with_clip_no_noise(self):
|
|
with self.cached_session() as sess:
|
|
record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0].
|
|
record2 = tf.constant([4.0, -3.0]) # Not clipped.
|
|
|
|
query = gaussian_query.GaussianSumQuery(
|
|
l2_norm_clip=5.0, stddev=0.0)
|
|
query_result, _ = test_utils.run_query(query, [record1, record2])
|
|
result = sess.run(query_result)
|
|
expected = [1.0, 1.0]
|
|
self.assertAllClose(result, expected)
|
|
|
|
def test_gaussian_sum_with_changing_clip_no_noise(self):
|
|
with self.cached_session() as sess:
|
|
record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0].
|
|
record2 = tf.constant([4.0, -3.0]) # Not clipped.
|
|
|
|
l2_norm_clip = tf.Variable(5.0)
|
|
l2_norm_clip_placeholder = tf.placeholder(tf.float32)
|
|
assign_l2_norm_clip = tf.assign(l2_norm_clip, l2_norm_clip_placeholder)
|
|
query = gaussian_query.GaussianSumQuery(
|
|
l2_norm_clip=l2_norm_clip, stddev=0.0)
|
|
query_result, _ = test_utils.run_query(query, [record1, record2])
|
|
|
|
self.evaluate(tf.global_variables_initializer())
|
|
result = sess.run(query_result)
|
|
expected = [1.0, 1.0]
|
|
self.assertAllClose(result, expected)
|
|
|
|
sess.run(assign_l2_norm_clip, {l2_norm_clip_placeholder: 0.0})
|
|
result = sess.run(query_result)
|
|
expected = [0.0, 0.0]
|
|
self.assertAllClose(result, expected)
|
|
|
|
def test_gaussian_sum_with_noise(self):
|
|
with self.cached_session() as sess:
|
|
record1, record2 = 2.71828, 3.14159
|
|
stddev = 1.0
|
|
|
|
query = gaussian_query.GaussianSumQuery(
|
|
l2_norm_clip=5.0, stddev=stddev)
|
|
query_result, _ = test_utils.run_query(query, [record1, record2])
|
|
|
|
noised_sums = []
|
|
for _ in xrange(1000):
|
|
noised_sums.append(sess.run(query_result))
|
|
|
|
result_stddev = np.std(noised_sums)
|
|
self.assertNear(result_stddev, stddev, 0.1)
|
|
|
|
def test_gaussian_average_no_noise(self):
|
|
with self.cached_session() as sess:
|
|
record1 = tf.constant([5.0, 0.0]) # Clipped to [3.0, 0.0].
|
|
record2 = tf.constant([-1.0, 2.0]) # Not clipped.
|
|
|
|
query = gaussian_query.GaussianAverageQuery(
|
|
l2_norm_clip=3.0, sum_stddev=0.0, denominator=2.0)
|
|
query_result, _ = test_utils.run_query(query, [record1, record2])
|
|
result = sess.run(query_result)
|
|
expected_average = [1.0, 1.0]
|
|
self.assertAllClose(result, expected_average)
|
|
|
|
def test_gaussian_average_with_noise(self):
|
|
with self.cached_session() as sess:
|
|
record1, record2 = 2.71828, 3.14159
|
|
sum_stddev = 1.0
|
|
denominator = 2.0
|
|
|
|
query = gaussian_query.GaussianAverageQuery(
|
|
l2_norm_clip=5.0, sum_stddev=sum_stddev, denominator=denominator)
|
|
query_result, _ = test_utils.run_query(query, [record1, record2])
|
|
|
|
noised_averages = []
|
|
for _ in range(1000):
|
|
noised_averages.append(sess.run(query_result))
|
|
|
|
result_stddev = np.std(noised_averages)
|
|
avg_stddev = sum_stddev / denominator
|
|
self.assertNear(result_stddev, avg_stddev, 0.1)
|
|
|
|
@parameterized.named_parameters(
|
|
('type_mismatch', [1.0], (1.0,), TypeError),
|
|
('too_few_on_left', [1.0], [1.0, 1.0], ValueError),
|
|
('too_few_on_right', [1.0, 1.0], [1.0], ValueError))
|
|
def test_incompatible_records(self, record1, record2, error_type):
|
|
query = gaussian_query.GaussianSumQuery(1.0, 0.0)
|
|
with self.assertRaises(error_type):
|
|
test_utils.run_query(query, [record1, record2])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|