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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
|
# Copyright 2018 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.
# ==============================================================================
"""Tests boosted_trees estimators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.estimator.python.estimator import boosted_trees
from tensorflow.core.kernels.boosted_trees import boosted_trees_pb2
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.estimator.canned import boosted_trees as canned_boosted_trees
from tensorflow.python.estimator.inputs import numpy_io
from tensorflow.python.feature_column import feature_column
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.platform import googletest
from tensorflow.python.training import checkpoint_utils
NUM_FEATURES = 3
BUCKET_BOUNDARIES = [-2., .5, 12.] # Boundaries for all the features.
INPUT_FEATURES = np.array(
[
[12.5, 1.0, -2.001, -2.0001, -1.999], # feature_0 quantized:[3,2,0,0,1]
[2.0, -3.0, 0.5, 0.0, 0.4995], # feature_1 quantized:[2,0,2,1,1]
[3.0, 20.0, 50.0, -100.0, 102.75], # feature_2 quantized:[2,3,3,0,3]
],
dtype=np.float32)
CLASSIFICATION_LABELS = [[0.], [1.], [1.], [0.], [0.]]
REGRESSION_LABELS = [[1.5], [0.3], [0.2], [2.], [5.]]
FEATURES_DICT = {'f_%d' % i: INPUT_FEATURES[i] for i in range(NUM_FEATURES)}
def _make_train_input_fn(is_classification):
"""Makes train input_fn for classification/regression."""
def _input_fn():
features_dict = dict(FEATURES_DICT)
labels = CLASSIFICATION_LABELS if is_classification else REGRESSION_LABELS
return features_dict, labels
return _input_fn
def _make_train_input_fn_dataset(is_classification):
"""Makes input_fn using Dataset."""
def _input_fn():
features_dict = dict(FEATURES_DICT)
labels = CLASSIFICATION_LABELS if is_classification else REGRESSION_LABELS
ds = dataset_ops.Dataset.zip(
(dataset_ops.Dataset.from_tensors(features_dict),
dataset_ops.Dataset.from_tensors(labels)
))
return ds
return _input_fn
class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase):
def setUp(self):
self._head = canned_boosted_trees._create_regression_head(label_dimension=1)
self._feature_columns = {
feature_column.bucketized_column(
feature_column.numeric_column('f_%d' % i, dtype=dtypes.float32),
BUCKET_BOUNDARIES)
for i in range(NUM_FEATURES)
}
def _assert_checkpoint(self, model_dir, global_step, finalized_trees,
attempted_layers):
reader = checkpoint_utils.load_checkpoint(model_dir)
self.assertEqual(global_step, reader.get_tensor(ops.GraphKeys.GLOBAL_STEP))
serialized = reader.get_tensor('boosted_trees:0_serialized')
ensemble_proto = boosted_trees_pb2.TreeEnsemble()
ensemble_proto.ParseFromString(serialized)
self.assertEqual(
finalized_trees,
sum([1 for t in ensemble_proto.tree_metadata if t.is_finalized]))
self.assertEqual(attempted_layers,
ensemble_proto.growing_metadata.num_layers_attempted)
def testTrainAndEvaluateEstimator(self):
input_fn = _make_train_input_fn(is_classification=False)
est = boosted_trees._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
n_trees=2,
head=self._head,
max_depth=5)
# It will stop after 10 steps because of the max depth and num trees.
num_steps = 100
# Train for a few steps, and validate final checkpoint.
est.train(input_fn, steps=num_steps)
self._assert_checkpoint(
est.model_dir, global_step=10, finalized_trees=2, attempted_layers=10)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 1.008551)
def testInferEstimator(self):
train_input_fn = _make_train_input_fn(is_classification=False)
predict_input_fn = numpy_io.numpy_input_fn(
x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False)
est = boosted_trees._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
n_trees=1,
max_depth=5,
head=self._head)
# It will stop after 5 steps because of the max depth and num trees.
num_steps = 100
# Train for a few steps, and validate final checkpoint.
est.train(train_input_fn, steps=num_steps)
self._assert_checkpoint(
est.model_dir, global_step=5, finalized_trees=1, attempted_layers=5)
# Validate predictions.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose(
[[0.571619], [0.262821], [0.124549], [0.956801], [1.769801]],
[pred['predictions'] for pred in predictions])
def testBinaryClassifierTrainInMemoryAndEvalAndInfer(self):
train_input_fn = _make_train_input_fn(is_classification=True)
predict_input_fn = numpy_io.numpy_input_fn(
x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False)
est = boosted_trees.boosted_trees_classifier_train_in_memory(
train_input_fn=train_input_fn, feature_columns=self._feature_columns,
n_trees=1, max_depth=5)
# It will stop after 5 steps because of the max depth and num trees.
self._assert_checkpoint(
est.model_dir, global_step=5, finalized_trees=1, attempted_layers=5)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['accuracy'], 1.0)
# Validate predictions.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose([[0], [1], [1], [0], [0]],
[pred['class_ids'] for pred in predictions])
def testBinaryClassifierTrainInMemoryWithDataset(self):
train_input_fn = _make_train_input_fn_dataset(is_classification=True)
predict_input_fn = numpy_io.numpy_input_fn(
x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False)
est = boosted_trees.boosted_trees_classifier_train_in_memory(
train_input_fn=train_input_fn, feature_columns=self._feature_columns,
n_trees=1, max_depth=5)
# It will stop after 5 steps because of the max depth and num trees.
self._assert_checkpoint(
est.model_dir, global_step=5, finalized_trees=1, attempted_layers=5)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['accuracy'], 1.0)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose([[0], [1], [1], [0], [0]],
[pred['class_ids'] for pred in predictions])
def testRegressorTrainInMemoryAndEvalAndInfer(self):
train_input_fn = _make_train_input_fn(is_classification=False)
predict_input_fn = numpy_io.numpy_input_fn(
x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False)
est = boosted_trees.boosted_trees_regressor_train_in_memory(
train_input_fn=train_input_fn, feature_columns=self._feature_columns,
n_trees=1, max_depth=5)
# It will stop after 5 steps because of the max depth and num trees.
self._assert_checkpoint(
est.model_dir, global_step=5, finalized_trees=1, attempted_layers=5)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 2.478283)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose(
[[0.571619], [0.262821], [0.124549], [0.956801], [1.769801]],
[pred['predictions'] for pred in predictions])
def testRegressorTrainInMemoryWithDataset(self):
train_input_fn = _make_train_input_fn_dataset(is_classification=False)
predict_input_fn = numpy_io.numpy_input_fn(
x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False)
est = boosted_trees.boosted_trees_regressor_train_in_memory(
train_input_fn=train_input_fn, feature_columns=self._feature_columns,
n_trees=1, max_depth=5)
# It will stop after 5 steps because of the max depth and num trees.
self._assert_checkpoint(
est.model_dir, global_step=5, finalized_trees=1, attempted_layers=5)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 2.478283)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose(
[[0.571619], [0.262821], [0.124549], [0.956801], [1.769801]],
[pred['predictions'] for pred in predictions])
if __name__ == '__main__':
googletest.main()
|
