1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Quantile ops python wrappers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from tensorflow.contrib.boosted_trees.python.ops import batch_ops_utils
# pylint: disable=unused-import
from tensorflow.contrib.boosted_trees.python.ops import boosted_trees_ops_loader
# pylint: enable=unused-import
from tensorflow.contrib.boosted_trees.python.ops import gen_quantile_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import,undefined-variable
from tensorflow.contrib.boosted_trees.python.ops.gen_quantile_ops import *
# pylint: enable=wildcard-import,undefined-variable
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import resources
from tensorflow.python.training import saver
# Pattern to remove all non alpha numeric from a string.
_PATTERN = re.compile(r"[\W_]+")
class QuantileAccumulator(saver.BaseSaverBuilder.SaveableObject):
"""A resource that allows distributed quantile computation."""
def __init__(self,
init_stamp_token,
epsilon,
num_quantiles,
max_elements=None,
name=None,
container=None,
generate_quantiles=False):
"""Creates a QuantileAccumulator object.
Args:
init_stamp_token: The initial value for the stamp token.
epsilon: Error bound on the quantile computation.
num_quantiles: Number of quantiles to produce from the final summary.
max_elements: Maximum number of elements added to the accumulator.
name: the name to save the accumulator under.
container: An optional `string`. Defaults to `""`
generate_quantiles: Generate quantiles instead of approximate boundaries.
If true, exactly `num_quantiles` will be produced in the final summary.
"""
self._epsilon = epsilon
self._generate_quantiles = generate_quantiles
name = _PATTERN.sub("", name)
with ops.name_scope(name, "QuantileAccumulator") as name:
self._quantile_accumulator_handle = (
gen_quantile_ops.quantile_stream_resource_handle_op(
container=container, shared_name=name, name=name))
self._create_op = gen_quantile_ops.create_quantile_accumulator(
self._quantile_accumulator_handle,
init_stamp_token,
epsilon=epsilon,
max_elements=max_elements,
num_quantiles=num_quantiles,
generate_quantiles=generate_quantiles)
is_initialized_op = gen_quantile_ops.quantile_accumulator_is_initialized(
self._quantile_accumulator_handle)
resources.register_resource(self._quantile_accumulator_handle,
self._create_op, is_initialized_op)
self._make_savable(name)
def _make_savable(self, name):
stamp_token, state, are_buckets_ready, buckets = (
gen_quantile_ops.quantile_accumulator_serialize(
self._quantile_accumulator_handle))
# slice_spec is useful for saving a slice from a variable.
# It's not meaningful in quantile accumulator.
slice_spec = ""
def make_save_spec(tensor, suffix):
return saver.BaseSaverBuilder.SaveSpec(tensor, slice_spec, name + suffix)
specs = [make_save_spec(stamp_token, "_stamp")]
specs += [make_save_spec(state, "_state")]
specs += [make_save_spec(are_buckets_ready, "_are_buckets_ready")]
specs += [make_save_spec(buckets, "buckets")]
super(QuantileAccumulator,
self).__init__(self._quantile_accumulator_handle, specs, name)
ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
def restore(self, restored_tensors, unused_restored_shapes):
"""Restores the associated quantile accumulator from 'restored_tensors'.
Args:
restored_tensors: the tensors that were loaded from a checkpoint.
unused_restored_shapes: the shapes this object should conform to after
restore.
Returns:
The operation that restores the state of the quantile accumulator.
"""
# Read the restored tensors with the same order that were added to saving
# spec.
stamp_token = restored_tensors[:1]
state = restored_tensors[1:2]
are_buckets_ready = restored_tensors[2:3]
buckets = restored_tensors[3]
with ops.control_dependencies([self._create_op]):
return gen_quantile_ops.quantile_accumulator_deserialize(
self._quantile_accumulator_handle,
stamp_token=stamp_token,
stream_state=state,
are_buckets_ready=are_buckets_ready,
buckets=buckets)
def get_buckets(self, stamp_token):
"""Returns quantile buckets created during previous flush."""
are_buckets_ready, buckets = (
gen_quantile_ops.quantile_accumulator_get_buckets(
quantile_accumulator_handles=[self._quantile_accumulator_handle],
stamp_token=stamp_token))
return are_buckets_ready[0], buckets[0]
def schedule_get_buckets(self):
"""Returns a scheduled read of buckets created during previous flush."""
return batch_ops_utils.ScheduledStampedResourceOp(
resource_handle=self._quantile_accumulator_handle,
op=gen_quantile_ops.quantile_accumulator_get_buckets)
def _make_summary(self, column, example_weights):
if isinstance(column, sparse_tensor.SparseTensor):
return gen_quantile_ops.make_quantile_summaries(
dense_float_features=[],
sparse_float_feature_indices=[column.indices],
sparse_float_feature_values=[column.values],
sparse_float_feature_shapes=[column.dense_shape],
example_weights=example_weights,
epsilon=self._epsilon / 2).sparse_summaries[0]
else:
return gen_quantile_ops.make_quantile_summaries(
dense_float_features=[column],
sparse_float_feature_indices=[],
sparse_float_feature_values=[],
sparse_float_feature_shapes=[],
example_weights=example_weights,
epsilon=self._epsilon / 2).dense_summaries[0]
def add_summary(self, stamp_token, column, example_weights):
"""Adds quantile summary to its stream in resource."""
summary = self._make_summary(column, example_weights)
return gen_quantile_ops.quantile_accumulator_add_summaries(
quantile_accumulator_handles=[self._quantile_accumulator_handle],
stamp_token=stamp_token,
summaries=[summary])
def add_prebuilt_summary(self, stamp_token, summary):
"""Adds quantile summary to its stream in resource."""
return gen_quantile_ops.quantile_accumulator_add_summaries(
quantile_accumulator_handles=[self._quantile_accumulator_handle],
stamp_token=stamp_token,
summaries=[summary])
def schedule_add_summary(self, stamp_token, column, example_weights):
"""Schedules to add a quantile summary to its stream in resource."""
summary = self._make_summary(column, example_weights)
return batch_ops_utils.ScheduledStampedResourceOp(
op=gen_quantile_ops.quantile_accumulator_add_summaries,
resource_handle=self._quantile_accumulator_handle,
summaries=summary)
def flush(self, stamp_token, next_stamp_token):
"""Finalizes quantile summary stream and resets it for next iteration.
Args:
stamp_token: Exepcted current token.
next_stamp_token: Next value for the token.
Returns:
A list of quantiles or approximate boundaries.
"""
return gen_quantile_ops.quantile_accumulator_flush(
quantile_accumulator_handle=self._quantile_accumulator_handle,
stamp_token=stamp_token,
next_stamp_token=next_stamp_token)
def flush_summary(self, stamp_token, next_stamp_token):
"""Finalizes quantile summary stream and resets it for next iteration."""
result = gen_quantile_ops.quantile_accumulator_flush_summary(
quantile_accumulator_handle=self._quantile_accumulator_handle,
stamp_token=stamp_token,
next_stamp_token=next_stamp_token)
return result
|
