forked from 626_privacy/tensorflow_privacy
(1) Merge CentralTreeSumQuery and DistributedTreeSumQuery into one DPQuery to modularize things. The new query takes in an inner_query argument. Depending on the behavior of inner query, the query will follow central DP or distributed DP.
(2) Remove the hard-coded L1 clipping and replace with norm bound checking in the inner query. This design allows us to use whatever clipping factory we want outside the DPQuery. PiperOrigin-RevId: 387236482
This commit is contained in:
A. Unique TensorFlower
parent
e7e11d14d9
commit
4d335d1b69
2 changed files with 204 additions and 390 deletions
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@ -15,21 +15,18 @@
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`TreeCumulativeSumQuery` and `TreeResidualSumQuery` are `DPQuery`s for continual
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online observation queries relying on `tree_aggregation`. 'Online' means that
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the leaf nodes of the tree arrive one by one as the time proceeds. The leaves
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are vector records as defined in `dp_query.DPQuery`.
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the leaf nodes of the tree arrive one by one as the time proceeds.
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`CentralTreeSumQuery` and `DistributedTreeSumQuery` are `DPQuery`s for
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central/distributed offline tree aggregation protocol. 'Offline' means all the
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leaf nodes are ready before the protocol starts. Each record, different from
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what is defined in `dp_query.DPQuery`, is a histogram (i.e. the leaf nodes).
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`TreeRangeSumQuery` is a `DPQuery`s for offline tree aggregation protocol.
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'Offline' means all the leaf nodes are ready before the protocol starts.
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"""
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import distutils
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import math
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from typing import Optional
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import attr
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import tensorflow as tf
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from tensorflow_privacy.privacy.dp_query import distributed_discrete_gaussian_query
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from tensorflow_privacy.privacy.dp_query import dp_query
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from tensorflow_privacy.privacy.dp_query import gaussian_query
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from tensorflow_privacy.privacy.dp_query import tree_aggregation
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@ -442,217 +439,84 @@ def _build_tree_from_leaf(leaf_nodes: tf.Tensor, arity: int) -> tf.RaggedTensor:
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return tree
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def _get_add_noise(stddev, seed: int = None):
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"""Utility function to decide which `add_noise` to use according to tf version."""
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if distutils.version.LooseVersion(
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tf.__version__) < distutils.version.LooseVersion('2.0.0'):
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class TreeRangeSumQuery(dp_query.SumAggregationDPQuery):
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"""Implements dp_query for accurate range queries using tree aggregation.
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# The seed should be only used for testing purpose.
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if seed is not None:
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tf.random.set_seed(seed)
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def add_noise(v):
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return v + tf.random.normal(
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tf.shape(input=v), stddev=stddev, dtype=v.dtype)
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else:
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random_normal = tf.random_normal_initializer(stddev=stddev, seed=seed)
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def add_noise(v):
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return v + tf.cast(random_normal(tf.shape(input=v)), dtype=v.dtype)
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return add_noise
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class CentralTreeSumQuery(dp_query.SumAggregationDPQuery):
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"""Implements dp_query for differentially private tree aggregation protocol.
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Implements a central variant of the tree aggregation protocol from the paper
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"'Is interaction necessary for distributed private learning?.' Adam Smith,
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Abhradeep Thakurta, Jalaj Upadhyay" by replacing their local randomizer with
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gaussian mechanism. The first step is to clip the clients' local updates (i.e.
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a 1-D array containing the leaf nodes of the tree) by L1 norm to make sure it
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does not exceed a prespecified upper bound. The second step is to construct
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the tree on the clipped update. The third step is to add independent gaussian
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noise to each node in the tree. The returned tree can support efficient and
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accurate range queries with differential privacy.
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Implements a variant of the tree aggregation protocol from. "Is interaction
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necessary for distributed private learning?. Adam Smith, Abhradeep Thakurta,
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Jalaj Upadhyay." Builds a tree on top of the input record and adds noise to
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the tree for differential privacy. Any range query can be decomposed into the
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sum of O(log(n)) nodes in the tree compared to O(n) when using a histogram.
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Improves efficiency and reduces noise scale.
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"""
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@attr.s(frozen=True)
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class GlobalState(object):
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"""Class defining global state for `CentralTreeSumQuery`.
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"""Class defining global state for TreeRangeSumQuery.
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Attributes:
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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"""
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l1_bound = attr.ib()
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def __init__(self,
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stddev: float,
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arity: int = 2,
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l1_bound: int = 10,
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seed: Optional[int] = None):
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"""Initializes the `CentralTreeSumQuery`.
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Args:
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stddev: The stddev of the noise added to each internal node of the
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constructed tree.
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arity: The branching factor of the tree.
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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seed: Random seed to generate Gaussian noise. Defaults to `None`. Only for
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test purpose.
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"""
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self._stddev = stddev
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self._arity = arity
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self._l1_bound = l1_bound
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self._seed = seed
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def initial_global_state(self):
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"""Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
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return CentralTreeSumQuery.GlobalState(l1_bound=self._l1_bound)
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def derive_sample_params(self, global_state):
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"""Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
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return global_state.l1_bound
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def preprocess_record(self, params, record):
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"""Implements `tensorflow_privacy.DPQuery.preprocess_record`."""
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casted_record = tf.cast(record, tf.float32)
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l1_norm = tf.norm(casted_record, ord=1)
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l1_bound = tf.cast(params, tf.float32)
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preprocessed_record, _ = tf.clip_by_global_norm([casted_record],
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l1_bound,
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use_norm=l1_norm)
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return preprocessed_record[0]
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def get_noised_result(self, sample_state, global_state):
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"""Implements `tensorflow_privacy.DPQuery.get_noised_result`.
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Args:
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sample_state: a frequency histogram.
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global_state: hyper-parameters of the query.
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Returns:
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a `tf.RaggedTensor` representing the tree built on top of `sample_state`.
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The jth node on the ith layer of the tree can be accessed by tree[i][j]
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where tree is the returned value.
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"""
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add_noise = _get_add_noise(self._stddev, self._seed)
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tree = _build_tree_from_leaf(sample_state, self._arity)
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return tf.map_fn(add_noise, tree), global_state
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class DistributedTreeSumQuery(dp_query.SumAggregationDPQuery):
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"""Implements dp_query for differentially private tree aggregation protocol.
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The difference from `CentralTreeSumQuery` is that the tree construction and
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gaussian noise addition happen in `preprocess_records`. The difference only
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takes effect when used together with
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`tff.aggregators.DifferentiallyPrivateFactory`. In other cases, this class
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should be treated as equal with `CentralTreeSumQuery`.
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Implements a distributed version of the tree aggregation protocol from. "Is
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interaction necessary for distributed private learning?." by replacing their
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local randomizer with gaussian mechanism. The first step is to check the L1
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norm of the clients' local updates (i.e. a 1-D array containing the leaf nodes
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of the tree) to make sure it does not exceed a prespecified upper bound. The
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second step is to construct the tree. The third step is to add independent
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gaussian noise to each node in the tree. The returned tree can support
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efficient and accurate range queries with differential privacy.
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"""
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@attr.s(frozen=True)
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class GlobalState(object):
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"""Class defining global state for DistributedTreeSumQuery.
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Attributes:
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stddev: The stddev of the noise added to each internal node in the
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constructed tree.
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arity: The branching factor of the tree (i.e. the number of children each
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internal node has).
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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inner_query_state: The global state of the inner query.
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"""
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stddev = attr.ib()
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arity = attr.ib()
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l1_bound = attr.ib()
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inner_query_state = attr.ib()
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def __init__(self,
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stddev: float,
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arity: int = 2,
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l1_bound: int = 10,
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seed: Optional[int] = None):
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"""Initializes the `DistributedTreeSumQuery`.
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inner_query: dp_query.SumAggregationDPQuery,
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arity: int = 2):
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"""Initializes the `TreeRangeSumQuery`.
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Args:
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stddev: The stddev of the noise added to each node in the tree.
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arity: The branching factor of the tree.
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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seed: Random seed to generate Gaussian noise. Defaults to `None`. Only for
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test purpose.
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inner_query: The inner `DPQuery` that adds noise to the tree.
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arity: The branching factor of the tree (i.e. the number of children each
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internal node has). Defaults to 2.
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"""
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self._stddev = stddev
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self._inner_query = inner_query
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self._arity = arity
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self._l1_bound = l1_bound
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self._seed = seed
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if self._arity < 1:
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raise ValueError(f'Invalid arity={arity} smaller than 2.')
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def initial_global_state(self):
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"""Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
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return DistributedTreeSumQuery.GlobalState(
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stddev=self._stddev, arity=self._arity, l1_bound=self._l1_bound)
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return TreeRangeSumQuery.GlobalState(
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arity=self._arity,
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inner_query_state=self._inner_query.initial_global_state())
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def derive_sample_params(self, global_state):
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"""Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
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return (global_state.stddev, global_state.arity, global_state.l1_bound)
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return (global_state.arity,
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self._inner_query.derive_sample_params(
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global_state.inner_query_state))
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def preprocess_record(self, params, record):
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"""Implements `tensorflow_privacy.DPQuery.preprocess_record`.
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This method clips the input record by L1 norm, constructs a tree on top of
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it, and adds gaussian noise to each node of the tree for differential
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privacy. Unlike `get_noised_result` in `CentralTreeSumQuery`, this function
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flattens the `tf.RaggedTensor` before outputting it. This is useful when
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used inside `tff.aggregators.DifferentiallyPrivateFactory` because it does
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not accept ragged output tensor.
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This method builds the tree, flattens it and applies
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`inner_query.preprocess_record` to the flattened tree.
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Args:
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params: hyper-parameters for preprocessing record, (stddev, aritry,
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l1_bound)
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record: leaf nodes for the tree.
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params: Hyper-parameters for preprocessing record.
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record: A histogram representing the leaf nodes of the tree.
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Returns:
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`tf.Tensor` representing the flattened version of the tree.
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A `tf.Tensor` representing the flattened version of the preprocessed tree.
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"""
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_, arity, l1_bound_ = params
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l1_bound = tf.cast(l1_bound_, tf.float32)
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casted_record = tf.cast(record, tf.float32)
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l1_norm = tf.norm(casted_record, ord=1)
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preprocessed_record, _ = tf.clip_by_global_norm([casted_record],
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l1_bound,
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use_norm=l1_norm)
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preprocessed_record = preprocessed_record[0]
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add_noise = _get_add_noise(self._stddev, self._seed)
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tree = _build_tree_from_leaf(preprocessed_record, arity)
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noisy_tree = tf.map_fn(add_noise, tree)
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arity, inner_query_params = params
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preprocessed_record = _build_tree_from_leaf(record, arity).flat_values
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preprocessed_record = self._inner_query.preprocess_record(
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inner_query_params, preprocessed_record)
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# The following codes reshape the output vector so the output shape of can
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# be statically inferred. This is useful when used with
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# `tff.aggregators.DifferentiallyPrivateFactory` because it needs to know
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# the output shape of this function statically and explicitly.
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flat_noisy_tree = noisy_tree.flat_values
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flat_tree_shape = [
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preprocessed_record_shape = [
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(self._arity**(math.ceil(math.log(record.shape[0], self._arity)) + 1) -
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1) // (self._arity - 1)
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]
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return tf.reshape(flat_noisy_tree, flat_tree_shape)
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return tf.reshape(preprocessed_record, preprocessed_record_shape)
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def get_noised_result(self, sample_state, global_state):
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"""Implements `tensorflow_privacy.DPQuery.get_noised_result`.
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@ -661,12 +525,11 @@ class DistributedTreeSumQuery(dp_query.SumAggregationDPQuery):
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output by `preprocess_records.`
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Args:
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sample_state: `tf.Tensor` for the flattened tree.
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global_state: hyper-parameters including noise multiplier, the branching
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factor of the tree and the maximum records per user.
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sample_state: A `tf.Tensor` for the flattened tree.
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global_state: The global state of the protocol.
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Returns:
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a `tf.RaggedTensor` for the tree.
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A `tf.RaggedTensor` representing the tree.
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"""
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# The [0] is needed because of how tf.RaggedTensor.from_two_splits works.
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# print(tf.RaggedTensor.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6],
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@ -682,3 +545,60 @@ class DistributedTreeSumQuery(dp_query.SumAggregationDPQuery):
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tree = tf.RaggedTensor.from_row_splits(
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values=sample_state, row_splits=row_splits)
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return tree, global_state
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@classmethod
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def build_central_gaussian_query(cls,
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l2_norm_clip: float,
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stddev: float,
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arity: int = 2):
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"""Returns `TreeRangeSumQuery` with central Gaussian noise.
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Args:
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l2_norm_clip: Each record should be clipped so that it has L2 norm at most
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`l2_norm_clip`.
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stddev: Stddev of the central Gaussian noise.
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arity: The branching factor of the tree (i.e. the number of children each
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internal node has). Defaults to 2.
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"""
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if l2_norm_clip <= 0:
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raise ValueError(f'`l2_norm_clip` must be positive, got {l2_norm_clip}.')
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if stddev < 0:
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raise ValueError(f'`stddev` must be non-negative, got {stddev}.')
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if arity < 2:
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raise ValueError(f'`arity` must be at least 2, got {arity}.')
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inner_query = gaussian_query.GaussianSumQuery(l2_norm_clip, stddev)
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return cls(arity=arity, inner_query=inner_query)
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@classmethod
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def build_distributed_discrete_gaussian_query(cls,
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l2_norm_bound: float,
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local_stddev: float,
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arity: int = 2):
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"""Returns `TreeRangeSumQuery` with central Gaussian noise.
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Args:
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l2_norm_bound: Each record should be clipped so that it has L2 norm at
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most `l2_norm_bound`.
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local_stddev: Scale/stddev of the local discrete Gaussian noise.
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arity: The branching factor of the tree (i.e. the number of children each
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internal node has). Defaults to 2.
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"""
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if l2_norm_bound <= 0:
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raise ValueError(
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f'`l2_clip_bound` must be positive, got {l2_norm_bound}.')
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if local_stddev < 0:
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raise ValueError(
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f'`local_stddev` must be non-negative, got {local_stddev}.')
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if arity < 2:
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raise ValueError(f'`arity` must be at least 2, got {arity}.')
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inner_query = distributed_discrete_gaussian_query.DistributedDiscreteGaussianSumQuery(
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l2_norm_bound, local_stddev)
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return cls(arity=arity, inner_query=inner_query)
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@ -423,111 +423,115 @@ class BuildTreeTest(tf.test.TestCase, parameterized.TestCase):
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self.assertEqual(tree[layer][idx], expected_value)
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class CentralTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
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class TreeRangeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
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def test_initial_global_state_type(self):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=NOISE_STD)
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global_state = query.initial_global_state()
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self.assertIsInstance(
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global_state, tree_aggregation_query.CentralTreeSumQuery.GlobalState)
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def test_derive_sample_params(self):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=NOISE_STD)
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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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self.assertAllClose(params, 10.)
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@parameterized.named_parameters(
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('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32)),
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('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32)),
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('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32)),
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('ternary_test_float', 3, tf.constant([1., 0., 0., 0.],
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dtype=tf.float32)),
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@parameterized.product(
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inner_query=['central', 'distributed'],
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params=[(0., 1., 2), (1., -1., 2), (1., 1., 1)],
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)
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def test_preprocess_record(self, arity, record):
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query = tree_aggregation_query.CentralTreeSumQuery(
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stddev=NOISE_STD, arity=arity)
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def test_raises_error(self, inner_query, params):
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clip_norm, stddev, arity = params
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with self.assertRaises(ValueError):
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if inner_query == 'central':
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tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
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clip_norm, stddev, arity)
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elif inner_query == 'distributed':
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tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
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clip_norm, stddev, arity)
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@parameterized.product(
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inner_query=['central', 'distributed'],
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clip_norm=[0.1, 1.0, 10.0],
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stddev=[0.1, 1.0, 10.0])
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def test_initial_global_state_type(self, inner_query, clip_norm, stddev):
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if inner_query == 'central':
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query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
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clip_norm, stddev)
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elif inner_query == 'distributed':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
|
||||
clip_norm, stddev)
|
||||
global_state = query.initial_global_state()
|
||||
self.assertIsInstance(global_state,
|
||||
tree_aggregation_query.TreeRangeSumQuery.GlobalState)
|
||||
|
||||
@parameterized.product(
|
||||
inner_query=['central', 'distributed'],
|
||||
clip_norm=[0.1, 1.0, 10.0],
|
||||
stddev=[0.1, 1.0, 10.0],
|
||||
arity=[2, 3, 4])
|
||||
def test_derive_sample_params(self, inner_query, clip_norm, stddev, arity):
|
||||
if inner_query == 'central':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
|
||||
clip_norm, stddev, arity)
|
||||
elif inner_query == 'distributed':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
|
||||
clip_norm, stddev, arity)
|
||||
global_state = query.initial_global_state()
|
||||
derived_arity, inner_query_state = query.derive_sample_params(global_state)
|
||||
self.assertAllClose(derived_arity, arity)
|
||||
if inner_query == 'central':
|
||||
self.assertAllClose(inner_query_state, clip_norm)
|
||||
elif inner_query == 'distributed':
|
||||
self.assertAllClose(inner_query_state.l2_norm_bound, clip_norm)
|
||||
self.assertAllClose(inner_query_state.local_stddev, stddev)
|
||||
|
||||
@parameterized.product(
|
||||
(dict(arity=2, expected_tree=[1, 1, 0, 1, 0, 0, 0]),
|
||||
dict(arity=3, expected_tree=[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])),
|
||||
inner_query=['central', 'distributed'],
|
||||
)
|
||||
def test_preprocess_record(self, inner_query, arity, expected_tree):
|
||||
if inner_query == 'central':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
|
||||
10., 0., arity)
|
||||
record = tf.constant([1, 0, 0, 0], dtype=tf.float32)
|
||||
expected_tree = tf.cast(expected_tree, tf.float32)
|
||||
elif inner_query == 'distributed':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
|
||||
10., 0., arity)
|
||||
record = tf.constant([1, 0, 0, 0], dtype=tf.int32)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
|
||||
self.assertAllClose(preprocessed_record, record)
|
||||
self.assertAllClose(preprocessed_record, expected_tree)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.constant([5, 5, 0, 0], dtype=tf.int32)),
|
||||
('binary_test_float', 2, tf.constant(
|
||||
[10., 10., 0., 0.],
|
||||
dtype=tf.float32), tf.constant([5., 5., 0., 0.], dtype=tf.float32)),
|
||||
('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.constant([5, 5, 0, 0], dtype=tf.int32)),
|
||||
('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.constant([5., 5., 0., 0.], dtype=tf.float32)),
|
||||
('stddev_1', 1, tf.constant([1, 0], dtype=tf.int32), [1, 1, 0]),
|
||||
('stddev_0_1', 4, tf.constant([1, 0], dtype=tf.int32), [1, 1, 0]),
|
||||
)
|
||||
def test_preprocess_record_clipped(self, arity, record,
|
||||
expected_clipped_value):
|
||||
query = tree_aggregation_query.CentralTreeSumQuery(
|
||||
stddev=NOISE_STD, arity=arity)
|
||||
def test_distributed_preprocess_record_with_noise(self, local_stddev, record,
|
||||
expected_tree):
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
|
||||
10., local_stddev)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
self.assertAllClose(preprocessed_record, expected_clipped_value)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
|
||||
('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
|
||||
('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[1.], [1., 0., 0.],
|
||||
[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
('ternary_test_float', 3, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([[1.], [1., 0., 0.],
|
||||
[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
)
|
||||
def test_get_noised_result(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.CentralTreeSumQuery(stddev=0., arity=arity)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
sample_state, global_state = query.get_noised_result(
|
||||
preprocessed_record, global_state)
|
||||
|
||||
self.assertAllClose(sample_state, expected_tree)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('stddev_0_01', 0.01, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
|
||||
('stddev_0_1', 0.1, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
|
||||
)
|
||||
def test_get_noised_result_with_noise(self, stddev, record, expected_tree):
|
||||
query = tree_aggregation_query.CentralTreeSumQuery(stddev=stddev, seed=0)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
|
||||
sample_state, _ = query.get_noised_result(preprocessed_record, global_state)
|
||||
|
||||
self.assertAllClose(
|
||||
sample_state.flat_values, expected_tree, atol=3 * stddev)
|
||||
preprocessed_record, expected_tree, atol=10 * local_stddev)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
|
||||
('binary_test_float', 2, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
|
||||
('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[10.], [10., 0., 0.],
|
||||
[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([[10.], [10., 0., 0.],
|
||||
[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
@parameterized.product(
|
||||
(dict(
|
||||
arity=2,
|
||||
expected_tree=tf.ragged.constant([[1], [1, 0], [1, 0, 0, 0]])),
|
||||
dict(
|
||||
arity=3,
|
||||
expected_tree=tf.ragged.constant([[1], [1, 0, 0],
|
||||
[1, 0, 0, 0, 0, 0, 0, 0, 0]]))),
|
||||
inner_query=['central', 'distributed'],
|
||||
)
|
||||
def test_get_noised_result_clipped(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.CentralTreeSumQuery(stddev=0., arity=arity)
|
||||
def test_get_noised_result(self, inner_query, arity, expected_tree):
|
||||
if inner_query == 'central':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
|
||||
10., 0., arity)
|
||||
record = tf.constant([1, 0, 0, 0], dtype=tf.float32)
|
||||
expected_tree = tf.cast(expected_tree, tf.float32)
|
||||
elif inner_query == 'distributed':
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
|
||||
10., 0., arity)
|
||||
record = tf.constant([1, 0, 0, 0], dtype=tf.int32)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
|
|
@ -536,128 +540,18 @@ class CentralTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
|
|||
|
||||
self.assertAllClose(sample_state, expected_tree)
|
||||
|
||||
|
||||
class DistributedTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
|
||||
|
||||
def test_initial_global_state_type(self):
|
||||
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(stddev=NOISE_STD)
|
||||
global_state = query.initial_global_state()
|
||||
self.assertIsInstance(
|
||||
global_state,
|
||||
tree_aggregation_query.DistributedTreeSumQuery.GlobalState)
|
||||
|
||||
def test_derive_sample_params(self):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(stddev=NOISE_STD)
|
||||
global_state = query.initial_global_state()
|
||||
stddev, arity, l1_bound = query.derive_sample_params(global_state)
|
||||
self.assertAllClose(stddev, NOISE_STD)
|
||||
self.assertAllClose(arity, 2)
|
||||
self.assertAllClose(l1_bound, 10)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([1., 1., 0., 1., 0., 0., 0.])),
|
||||
('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([1., 1., 0., 1., 0., 0., 0.])),
|
||||
('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.
|
||||
])),
|
||||
('ternary_test_float', 3, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.
|
||||
])),
|
||||
)
|
||||
def test_preprocess_record(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
@parameterized.product(stddev=[0.1, 1.0, 10.0])
|
||||
def test_central_get_noised_result_with_noise(self, stddev):
|
||||
query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
|
||||
10., stddev)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
self.assertAllClose(preprocessed_record, expected_tree)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('stddev_0_01', 0.01, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
|
||||
('stddev_0_1', 0.1, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
|
||||
)
|
||||
def test_preprocess_record_with_noise(self, stddev, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=stddev, seed=0)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
|
||||
self.assertAllClose(preprocessed_record, expected_tree, atol=3 * stddev)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([10., 10., 0., 5., 5., 0., 0.])),
|
||||
('binary_test_float', 2, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([10., 10., 0., 5., 5., 0., 0.])),
|
||||
('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant(
|
||||
[10., 10., 0., 0., 5., 5., 0., 0., 0., 0., 0., 0., 0.])),
|
||||
('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant(
|
||||
[10., 10., 0., 0., 5., 5., 0., 0., 0., 0., 0., 0., 0.])),
|
||||
)
|
||||
def test_preprocess_record_clipped(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
self.assertAllClose(preprocessed_record, expected_tree)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
|
||||
('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
|
||||
('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[1.], [1., 0., 0.],
|
||||
[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
('ternary_test_float', 3, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([[1.], [1., 0., 0.],
|
||||
[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
)
|
||||
def test_get_noised_result(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
preprocessed_record = query.preprocess_record(params, tf.constant([1., 0.]))
|
||||
sample_state, global_state = query.get_noised_result(
|
||||
preprocessed_record, global_state)
|
||||
|
||||
self.assertAllClose(sample_state, expected_tree)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
|
||||
('binary_test_float', 2, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
|
||||
('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[10.], [10., 0., 0.],
|
||||
[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([[10.], [10., 0., 0.],
|
||||
[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
)
|
||||
def test_get_noised_result_clipped(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
global_state = query.initial_global_state()
|
||||
params = query.derive_sample_params(global_state)
|
||||
preprocessed_record = query.preprocess_record(params, record)
|
||||
sample_state, global_state = query.get_noised_result(
|
||||
preprocessed_record, global_state)
|
||||
|
||||
self.assertAllClose(sample_state, expected_tree)
|
||||
self.assertAllClose(
|
||||
sample_state, tf.ragged.constant([[1.], [1., 0.]]), atol=10 * stddev)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
Loading…
Reference in a new issue