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# Copyright 2016 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 for tf.contrib.tensor_forest.ops.sample_inputs_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.tensor_forest.python.ops import training_ops
from tensorflow.python.framework import test_util
from tensorflow.python.platform import googletest
class SampleInputsTest(test_util.TensorFlowTestCase):
def setUp(self):
self.input_data = [[-1., 10.], [-10., 2.], # node 1
[20., 50.], [1., -2.]] # node 2
self.node_map = [-1, 0, 1]
self.leaves = [1, 1, 2, 2]
self.split_features = [[-1, -1, -1], [1, 0, -1], [-1, -1, -1]]
self.split_thresholds = [[0., 0., 0.], [5., -2., 0.], [0., 0., 0.]]
self.ops = training_ops.Load()
def testSimple(self):
with self.test_session():
tf.initialize_all_variables().run()
indices, feature_updates, threshold_updates = (
self.ops.sample_inputs(
self.input_data, self.node_map, self.leaves, self.split_features,
self.split_thresholds, split_initializations_per_input=1,
split_sampling_random_seed=3))
self.assertAllEqual([1, 0], indices.eval())
self.assertAllEqual([[1, 0, 1], [0, 0, -1]],
feature_updates.eval())
self.assertAllEqual([[5., -2., 50.], [-1., -10., 0.]],
threshold_updates.eval())
def testNoAccumulators(self):
with self.test_session():
tf.initialize_all_variables().run()
indices, feature_updates, threshold_updates = (
self.ops.sample_inputs(
self.input_data, [-1] * 3, self.leaves, self.split_features,
self.split_thresholds, split_initializations_per_input=1,
split_sampling_random_seed=3))
self.assertAllEqual([], indices.eval())
self.assertAllEqual((0, 3), feature_updates.eval().shape)
self.assertAllEqual((0, 3), threshold_updates.eval().shape)
def testBadInput(self):
del self.split_features[1]
with self.test_session():
tf.initialize_all_variables().run()
with self.assertRaisesOpError(
'split_features and split_thresholds should be the same shape.'):
indices, _, _ = self.ops.sample_inputs(
self.input_data, self.node_map, self.leaves, self.split_features,
self.split_thresholds, split_initializations_per_input=1,
split_sampling_random_seed=3)
self.assertAllEqual([], indices.eval())
if __name__ == '__main__':
googletest.main()
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