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
102 lines
3.3 KiB
Python
102 lines
3.3 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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"""Implements DPQuery interface for normalized 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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import tensorflow as tf
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from privacy.dp_query import dp_query
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nest = tf.contrib.framework.nest
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class NormalizedQuery(dp_query.DPQuery):
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"""DPQuery for queries with a DPQuery numerator and fixed denominator."""
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def __init__(self, numerator_query, denominator):
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"""Initializer for NormalizedQuery.
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Args:
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numerator_query: A DPQuery for the numerator.
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denominator: A value for the denominator.
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"""
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self._numerator = numerator_query
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self._denominator = tf.to_float(denominator)
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def initial_global_state(self):
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"""Returns the initial global state for the NormalizedQuery."""
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# NormalizedQuery has no global state beyond the numerator state.
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return self._numerator.initial_global_state()
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def derive_sample_params(self, global_state):
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"""Given the global state, derives parameters to use for the next sample.
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Args:
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global_state: The current global state.
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Returns:
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Parameters to use to process records in the next sample.
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"""
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return self._numerator.derive_sample_params(global_state)
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def initial_sample_state(self, global_state, tensors):
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"""Returns an initial state to use for the next sample.
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Args:
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global_state: The current global state.
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tensors: A structure of tensors used as a template to create the initial
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sample state.
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Returns: An initial sample state.
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"""
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# NormalizedQuery has no sample state beyond the numerator state.
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return self._numerator.initial_sample_state(global_state, tensors)
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def accumulate_record(self, params, sample_state, record):
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"""Accumulates a single record into the sample state.
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Args:
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params: The parameters for the sample.
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sample_state: The current sample state.
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record: The record to accumulate.
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Returns:
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The updated sample state.
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"""
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return self._numerator.accumulate_record(params, sample_state, record)
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def get_noised_result(self, sample_state, global_state):
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"""Gets noised average after all records of sample have been accumulated.
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Args:
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sample_state: The sample state after all records have been accumulated.
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global_state: The global state.
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Returns:
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A tuple (estimate, new_global_state) where "estimate" is the estimated
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average of the records and "new_global_state" is the updated global state.
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"""
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noised_sum, new_sum_global_state = self._numerator.get_noised_result(
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sample_state, global_state)
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def normalize(v):
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return tf.truediv(v, self._denominator)
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return nest.map_structure(normalize, noised_sum), new_sum_global_state
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