forked from 626_privacy/tensorflow_privacy
6231d0802d
Moved query classes from dir optimizers into new dir dp_query. Added NormalizedQuery class for queries that divide the output of another query by a constant like GaussianAverageQuery. PiperOrigin-RevId: 240167115
49 lines
1.9 KiB
Python
49 lines
1.9 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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"""Utility methods for testing private queries.
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Utility methods for testing private queries.
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"""
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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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def run_query(query, records, global_state=None, weights=None):
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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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global_state: The current global state. If None, an initial global state is
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generated.
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weights: An optional iterable containing the weights of the records.
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Returns:
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A tuple (result, new_global_state) where "result" is the result of the
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query and "new_global_state" is the updated global state.
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"""
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if not global_state:
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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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if weights is None:
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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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else:
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for weight, record in zip(weights, records):
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sample_state = query.accumulate_record(
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params, sample_state, record, weight)
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return query.get_noised_result(sample_state, global_state)
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