diff options
Diffstat (limited to 'tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py')
| -rw-r--r-- | tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py | 351 |
1 files changed, 0 insertions, 351 deletions
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py deleted file mode 100644 index 842e0caeca..0000000000 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/ensemble_optimizer_ops_test.py +++ /dev/null @@ -1,351 +0,0 @@ -# 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. -# ============================================================================== -"""Tests for the GTFlow ensemble optimization ops. - -The tests cover: -- Adding a newly built tree to an existing ensemble -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.boosted_trees.proto import tree_config_pb2 -from tensorflow.contrib.boosted_trees.python.ops import ensemble_optimizer_ops -from tensorflow.contrib.boosted_trees.python.ops import model_ops -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import resources -from tensorflow.python.ops import variables -from tensorflow.python.platform import googletest - - -def _append_to_leaf(leaf, class_id, weight): - """Helper method for building tree leaves. - - Appends weight contributions for the given class index to a leaf node. - - Args: - leaf: leaf node to append to, int - class_id: class Id for the weight update, int - weight: weight contribution value, float - """ - leaf.sparse_vector.index.append(class_id) - leaf.sparse_vector.value.append(weight) - - -class EnsembleOptimizerOpsTest(test_util.TensorFlowTestCase): - - def setUp(self): - """Create an ensemble of 2 trees.""" - super(EnsembleOptimizerOpsTest, self).setUp() - self._tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - # First tree. - tree_1 = self._tree_ensemble.trees.add() - _append_to_leaf(tree_1.nodes.add().leaf, 0, 0.4) - _append_to_leaf(tree_1.nodes.add().leaf, 1, 0.6) - # Second tree. - tree_2 = self._tree_ensemble.trees.add() - _append_to_leaf(tree_2.nodes.add().leaf, 0, 1) - _append_to_leaf(tree_2.nodes.add().leaf, 1, 0) - - self._tree_ensemble.tree_weights.append(1.0) - self._tree_ensemble.tree_weights.append(1.0) - - meta_1 = self._tree_ensemble.tree_metadata.add() - meta_1.num_tree_weight_updates = 2 - meta_2 = self._tree_ensemble.tree_metadata.add() - meta_2.num_tree_weight_updates = 3 - - # Ensemble to be added. - self._ensemble_to_add = tree_config_pb2.DecisionTreeEnsembleConfig() - - self._tree_to_add = self._ensemble_to_add.trees.add() - _append_to_leaf(self._tree_to_add.nodes.add().leaf, 0, 0.3) - _append_to_leaf(self._tree_to_add.nodes.add().leaf, 1, 0.7) - - def testWithEmptyEnsemble(self): - with self.test_session(): - # Create an empty ensemble. - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, tree_ensemble_config="", name="empty") - - # Create zero feature importance. - feature_usage_counts = variables.Variable( - initial_value=array_ops.zeros([1], dtypes.int64), - name="feature_usage_counts", - trainable=False) - feature_gains = variables.Variable( - initial_value=array_ops.zeros([1], dtypes.float32), - name="feature_gains", - trainable=False) - - resources.initialize_resources(resources.shared_resources()).run() - variables.initialize_all_variables().run() - - with ops.control_dependencies([ - ensemble_optimizer_ops.add_trees_to_ensemble( - tree_ensemble_handle, - self._ensemble_to_add.SerializeToString(), - feature_usage_counts, [2], - feature_gains, [0.4], [[]], - learning_rate=1.0) - ]): - result = model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1] - - # Output. - output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - output_ensemble.ParseFromString(result.eval()) - self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[0]) - self.assertEqual(1, len(output_ensemble.trees)) - - self.assertAllEqual([1.0], output_ensemble.tree_weights) - - self.assertEqual(1, - output_ensemble.tree_metadata[0].num_tree_weight_updates) - - self.assertAllEqual([2], feature_usage_counts.eval()) - self.assertArrayNear([0.4], feature_gains.eval(), 1e-6) - - def testWithExistingEnsemble(self): - with self.test_session(): - # Create existing tree ensemble. - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=self._tree_ensemble.SerializeToString(), - name="existing") - # Create non-zero feature importance. - feature_usage_counts = variables.Variable( - initial_value=np.array([0, 4, 1], np.int64), - name="feature_usage_counts", - trainable=False) - feature_gains = variables.Variable( - initial_value=np.array([0.0, 0.3, 0.05], np.float32), - name="feature_gains", - trainable=False) - - resources.initialize_resources(resources.shared_resources()).run() - variables.initialize_all_variables().run() - output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - with ops.control_dependencies([ - ensemble_optimizer_ops.add_trees_to_ensemble( - tree_ensemble_handle, - self._ensemble_to_add.SerializeToString(), - feature_usage_counts, [1, 2, 0], - feature_gains, [0.02, 0.1, 0.0], [[], []], - learning_rate=1) - ]): - output_ensemble.ParseFromString( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval()) - - # Output. - self.assertEqual(3, len(output_ensemble.trees)) - self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[2]) - - self.assertAllEqual([1.0, 1.0, 1.0], output_ensemble.tree_weights) - - self.assertEqual(2, - output_ensemble.tree_metadata[0].num_tree_weight_updates) - self.assertEqual(3, - output_ensemble.tree_metadata[1].num_tree_weight_updates) - self.assertEqual(1, - output_ensemble.tree_metadata[2].num_tree_weight_updates) - self.assertAllEqual([1, 6, 1], feature_usage_counts.eval()) - self.assertArrayNear([0.02, 0.4, 0.05], feature_gains.eval(), 1e-6) - - def testWithExistingEnsembleAndDropout(self): - with self.test_session(): - tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - # Add 10 trees with some weights. - for i in range(0, 10): - tree = tree_ensemble.trees.add() - _append_to_leaf(tree.nodes.add().leaf, 0, -0.4) - tree_ensemble.tree_weights.append(i + 1) - meta = tree_ensemble.tree_metadata.add() - meta.num_tree_weight_updates = 1 - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble.SerializeToString(), - name="existing") - # Create non-zero feature importance. - feature_usage_counts = variables.Variable( - initial_value=np.array([2, 3], np.int64), - name="feature_usage_counts", - trainable=False) - feature_gains = variables.Variable( - initial_value=np.array([0.0, 0.3], np.float32), - name="feature_gains", - trainable=False) - - resources.initialize_resources(resources.shared_resources()).run() - variables.initialize_all_variables().run() - - dropped = [1, 6, 8] - dropped_original_weights = [2.0, 7.0, 9.0] - - output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - with ops.control_dependencies([ - ensemble_optimizer_ops.add_trees_to_ensemble( - tree_ensemble_handle, - self._ensemble_to_add.SerializeToString(), - feature_usage_counts, [1, 2], - feature_gains, [0.5, 0.3], [dropped, dropped_original_weights], - learning_rate=0.1) - ]): - output_ensemble.ParseFromString( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval()) - - # Output. - self.assertEqual(11, len(output_ensemble.trees)) - self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[10]) - self.assertAllClose(4.5, output_ensemble.tree_weights[10]) - - self.assertAllClose([1., 1.5, 3., 4., 5., 6., 5.25, 8., 6.75, 10., 4.5], - output_ensemble.tree_weights) - - self.assertEqual(1, - output_ensemble.tree_metadata[0].num_tree_weight_updates) - self.assertEqual(2, - output_ensemble.tree_metadata[1].num_tree_weight_updates) - self.assertEqual(1, - output_ensemble.tree_metadata[2].num_tree_weight_updates) - - self.assertEqual(1, - output_ensemble.tree_metadata[3].num_tree_weight_updates) - self.assertEqual(1, - output_ensemble.tree_metadata[4].num_tree_weight_updates) - self.assertEqual(1, - output_ensemble.tree_metadata[5].num_tree_weight_updates) - self.assertEqual(2, - output_ensemble.tree_metadata[6].num_tree_weight_updates) - self.assertEqual(1, - output_ensemble.tree_metadata[7].num_tree_weight_updates) - self.assertEqual(2, - output_ensemble.tree_metadata[8].num_tree_weight_updates) - self.assertEqual(1, - output_ensemble.tree_metadata[9].num_tree_weight_updates) - self.assertEqual( - 1, output_ensemble.tree_metadata[10].num_tree_weight_updates) - self.assertAllEqual([3, 5], feature_usage_counts.eval()) - self.assertArrayNear([0.05, 0.33], feature_gains.eval(), 1e-6) - - def testWithEmptyEnsembleAndShrinkage(self): - with self.test_session(): - # Add shrinkage config. - learning_rate = 0.0001 - tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble.SerializeToString(), - name="existing") - - # Create zero feature importance. - feature_usage_counts = variables.Variable( - initial_value=np.array([0, 0], np.int64), - name="feature_usage_counts", - trainable=False) - feature_gains = variables.Variable( - initial_value=np.array([0.0, 0.0], np.float32), - name="feature_gains", - trainable=False) - - resources.initialize_resources(resources.shared_resources()).run() - variables.initialize_all_variables().run() - - output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - with ops.control_dependencies([ - ensemble_optimizer_ops.add_trees_to_ensemble( - tree_ensemble_handle, - self._ensemble_to_add.SerializeToString(), - feature_usage_counts, [1, 2], - feature_gains, [0.5, 0.3], [[], []], - learning_rate=learning_rate) - ]): - output_ensemble.ParseFromString( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval()) - - # New tree is added with shrinkage weight. - self.assertAllClose([learning_rate], output_ensemble.tree_weights) - self.assertEqual(1, - output_ensemble.tree_metadata[0].num_tree_weight_updates) - self.assertAllEqual([1, 2], feature_usage_counts.eval()) - self.assertArrayNear([0.5 * learning_rate, 0.3 * learning_rate], - feature_gains.eval(), 1e-6) - - def testWithExistingEnsembleAndShrinkage(self): - with self.test_session(): - # Add shrinkage config. - learning_rate = 0.0001 - tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - # Add 10 trees with some weights. - for i in range(0, 5): - tree = tree_ensemble.trees.add() - _append_to_leaf(tree.nodes.add().leaf, 0, -0.4) - tree_ensemble.tree_weights.append(i + 1) - meta = tree_ensemble.tree_metadata.add() - meta.num_tree_weight_updates = 1 - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble.SerializeToString(), - name="existing") - - # Create non-zero feature importance. - feature_usage_counts = variables.Variable( - initial_value=np.array([4, 7], np.int64), - name="feature_usage_counts", - trainable=False) - feature_gains = variables.Variable( - initial_value=np.array([0.2, 0.8], np.float32), - name="feature_gains", - trainable=False) - - resources.initialize_resources(resources.shared_resources()).run() - variables.initialize_all_variables().run() - - output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig() - with ops.control_dependencies([ - ensemble_optimizer_ops.add_trees_to_ensemble( - tree_ensemble_handle, - self._ensemble_to_add.SerializeToString(), - feature_usage_counts, [1, 2], - feature_gains, [0.5, 0.3], [[], []], - learning_rate=learning_rate) - ]): - output_ensemble.ParseFromString( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval()) - - # The weights of previous trees stayed the same, new tree (LAST) is added - # with shrinkage weight. - self.assertAllClose([1.0, 2.0, 3.0, 4.0, 5.0, learning_rate], - output_ensemble.tree_weights) - - # Check that all number of updates are equal to 1 (e,g, no old tree weight - # got adjusted. - for i in range(0, 6): - self.assertEqual( - 1, output_ensemble.tree_metadata[i].num_tree_weight_updates) - - # Ensure feature importance was aggregated correctly. - self.assertAllEqual([5, 9], feature_usage_counts.eval()) - self.assertArrayNear( - [0.2 + 0.5 * learning_rate, 0.8 + 0.3 * learning_rate], - feature_gains.eval(), 1e-6) - -if __name__ == "__main__": - googletest.main() |