130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
# Copyright 2018, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for GaussianAverageQuery."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl.testing import parameterized
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import numpy as np
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import tensorflow as tf
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from privacy.optimizers import gaussian_query
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def _run_query(query, records):
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"""Executes query on the given set of records as a single sample.
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Args:
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query: A PrivateQuery to run.
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records: An iterable containing records to pass to the query.
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Returns:
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The result of the query.
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"""
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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sample_state = query.initial_sample_state(global_state, next(iter(records)))
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for record in records:
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sample_state = query.accumulate_record(params, sample_state, record)
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result, _ = query.get_query_result(sample_state, global_state)
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return result
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class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
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def test_gaussian_sum_no_clip_no_noise(self):
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with self.cached_session() as sess:
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record1 = tf.constant([2.0, 0.0])
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record2 = tf.constant([-1.0, 1.0])
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query = gaussian_query.GaussianSumQuery(
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l2_norm_clip=10.0, stddev=0.0)
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query_result = _run_query(query, [record1, record2])
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result = sess.run(query_result)
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expected = [1.0, 1.0]
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self.assertAllClose(result, expected)
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def test_gaussian_sum_with_clip_no_noise(self):
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with self.cached_session() as sess:
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record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0].
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record2 = tf.constant([4.0, -3.0]) # Not clipped.
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query = gaussian_query.GaussianSumQuery(
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l2_norm_clip=5.0, stddev=0.0)
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query_result = _run_query(query, [record1, record2])
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result = sess.run(query_result)
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expected = [1.0, 1.0]
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self.assertAllClose(result, expected)
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def test_gaussian_sum_with_noise(self):
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with self.cached_session() as sess:
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record1, record2 = 2.71828, 3.14159
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stddev = 1.0
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query = gaussian_query.GaussianSumQuery(
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l2_norm_clip=5.0, stddev=stddev)
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query_result = _run_query(query, [record1, record2])
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noised_sums = []
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for _ in xrange(1000):
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noised_sums.append(sess.run(query_result))
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result_stddev = np.std(noised_sums)
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self.assertNear(result_stddev, stddev, 0.1)
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def test_gaussian_average_no_noise(self):
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with self.cached_session() as sess:
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record1 = tf.constant([5.0, 0.0]) # Clipped to [3.0, 0.0].
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record2 = tf.constant([-1.0, 2.0]) # Not clipped.
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query = gaussian_query.GaussianAverageQuery(
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l2_norm_clip=3.0, sum_stddev=0.0, denominator=2.0)
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query_result = _run_query(query, [record1, record2])
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result = sess.run(query_result)
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expected_average = [1.0, 1.0]
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self.assertAllClose(result, expected_average)
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def test_gaussian_average_with_noise(self):
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with self.cached_session() as sess:
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record1, record2 = 2.71828, 3.14159
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sum_stddev = 1.0
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denominator = 2.0
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query = gaussian_query.GaussianAverageQuery(
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l2_norm_clip=5.0, sum_stddev=sum_stddev, denominator=denominator)
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query_result = _run_query(query, [record1, record2])
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noised_averages = []
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for _ in range(1000):
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noised_averages.append(sess.run(query_result))
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result_stddev = np.std(noised_averages)
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avg_stddev = sum_stddev / denominator
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self.assertNear(result_stddev, avg_stddev, 0.1)
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@parameterized.named_parameters(
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('type_mismatch', [1.0], (1.0,), TypeError),
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('too_few_on_left', [1.0], [1.0, 1.0], ValueError),
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('too_few_on_right', [1.0, 1.0], [1.0], ValueError))
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def test_incompatible_records(self, record1, record2, error_type):
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query = gaussian_query.GaussianSumQuery(1.0, 0.0)
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with self.assertRaises(error_type):
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_run_query(query, [record1, record2])
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if __name__ == '__main__':
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tf.test.main()
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