forked from 626_privacy/tensorflow_privacy
1d1a6e087a
1. Split DPQuery.accumulate_record function into preprocess_record and accumulate_preprocessed_record. 2. Add merge_sample_state function. 3. Add default implementations for some functions in DPQuery, and add base class SumAggregationDPQuery that implements some more. Only get_noised_result is still abstract. 4. Enforce that all states and parameters used as inputs and outputs to DPQuery functions are nested structures of tensors by replacing numbers with constants and Nones with empty tuples. PiperOrigin-RevId: 247975791
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# Copyright 2019, 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 NoPrivacyAverageQuery."""
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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 tensorflow as tf
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from privacy.dp_query import no_privacy_query
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from privacy.dp_query import test_utils
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class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
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def test_sum(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 = no_privacy_query.NoPrivacySumQuery()
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query_result, _ = test_utils.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_no_privacy_average(self):
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with self.cached_session() as sess:
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record1 = tf.constant([5.0, 0.0])
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record2 = tf.constant([-1.0, 2.0])
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query = no_privacy_query.NoPrivacyAverageQuery()
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query_result, _ = test_utils.run_query(query, [record1, record2])
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result = sess.run(query_result)
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expected = [2.0, 1.0]
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self.assertAllClose(result, expected)
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def test_no_privacy_weighted_average(self):
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with self.cached_session() as sess:
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record1 = tf.constant([4.0, 0.0])
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record2 = tf.constant([-1.0, 1.0])
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weights = [1, 3]
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query = no_privacy_query.NoPrivacyAverageQuery()
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query_result, _ = test_utils.run_query(
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query, [record1, record2], weights=weights)
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result = sess.run(query_result)
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expected = [0.25, 0.75]
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self.assertAllClose(result, expected)
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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 = no_privacy_query.NoPrivacySumQuery()
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with self.assertRaises(error_type):
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test_utils.run_query(query, [record1, record2])
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if __name__ == '__main__':
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tf.test.main()
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