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# 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.
# ==============================================================================
"""Test for checking quantile related ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import boosted_trees_ops
from tensorflow.python.ops import resources
from tensorflow.python.ops.gen_boosted_trees_ops import boosted_trees_quantile_stream_resource_handle_op as resource_handle_op
from tensorflow.python.ops.gen_boosted_trees_ops import is_boosted_trees_quantile_stream_resource_initialized as resource_initialized
from tensorflow.python.platform import googletest
class QuantileOpsTest(test_util.TensorFlowTestCase):
def create_resource(self, name, eps, max_elements, num_streams=1):
quantile_accumulator_handle = resource_handle_op(
container="", shared_name=name, name=name)
create_op = boosted_trees_ops.create_quantile_stream_resource(
quantile_accumulator_handle,
epsilon=eps,
max_elements=max_elements,
num_streams=num_streams)
is_initialized_op = resource_initialized(quantile_accumulator_handle)
resources.register_resource(quantile_accumulator_handle, create_op,
is_initialized_op)
return quantile_accumulator_handle
def setUp(self):
"""Sets up the quantile ops test as follows.
Create a batch of 6 examples having 2 features
The data looks like this
| Instance | instance weights | Feature 0 | Feature 1
| 0 | 10 | 1.2 | 2.3
| 1 | 1 | 12.1 | 1.2
| 2 | 1 | 0.3 | 1.1
| 3 | 1 | 0.5 | 2.6
| 4 | 1 | 0.6 | 3.2
| 5 | 1 | 2.2 | 0.8
"""
self._feature_0 = constant_op.constant(
[[1.2], [12.1], [0.3], [0.5], [0.6], [2.2]], dtype=dtypes.float32)
self._feature_1 = constant_op.constant(
[[2.3], [1.2], [1.1], [2.6], [3.2], [0.8]], dtype=dtypes.float32)
self._feature_0_boundaries = constant_op.constant(
[0.3, 0.6, 1.2, 12.1], dtype=dtypes.float32)
self._feature_1_boundaries = constant_op.constant(
[0.8, 1.2, 2.3, 3.2], dtype=dtypes.float32)
self._feature_0_quantiles = constant_op.constant(
[[2], [3], [0], [1], [1], [3]], dtype=dtypes.int32)
self._feature_1_quantiles = constant_op.constant(
[[2], [1], [1], [3], [3], [0]], dtype=dtypes.int32)
self._example_weights = constant_op.constant(
[10, 1, 1, 1, 1, 1], dtype=dtypes.float32)
self.eps = 0.01
self.max_elements = 1 << 16
self.num_quantiles = constant_op.constant(3, dtype=dtypes.int64)
def testBasicQuantileBucketsSingleResource(self):
with self.test_session() as sess:
quantile_accumulator_handle = self.create_resource("floats", self.eps,
self.max_elements, 2)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, summaries)
flush_op = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle, self.num_quantiles)
buckets = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle, num_features=2)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], buckets)
sess.run(summary_op)
sess.run(flush_op)
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testBasicQuantileBucketsMultipleResources(self):
with self.test_session() as sess:
quantile_accumulator_handle_0 = self.create_resource("float_0", self.eps,
self.max_elements)
quantile_accumulator_handle_1 = self.create_resource("float_1", self.eps,
self.max_elements)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op_0 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_0,
[summaries[0]])
summary_op_1 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_1,
[summaries[1]])
flush_op_0 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_0, self.num_quantiles)
flush_op_1 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_1, self.num_quantiles)
bucket_0 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_0, num_features=1)
bucket_1 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_1, num_features=1)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], bucket_0 + bucket_1)
sess.run([summary_op_0, summary_op_1])
sess.run([flush_op_0, flush_op_1])
self.assertAllClose(self._feature_0_boundaries, bucket_0[0].eval())
self.assertAllClose(self._feature_1_boundaries, bucket_1[0].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
if __name__ == "__main__":
googletest.main()
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