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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.
// =============================================================================
#include "tensorflow/contrib/boosted_trees/lib/trees/decision_tree.h"
#include "tensorflow/core/platform/macros.h"
#include <algorithm>
namespace tensorflow {
namespace boosted_trees {
namespace trees {
constexpr int kInvalidLeaf = -1;
int DecisionTree::Traverse(const DecisionTreeConfig& config,
const int32 sub_root_id,
const utils::Example& example) {
if (TF_PREDICT_FALSE(config.nodes_size() <= sub_root_id)) {
return kInvalidLeaf;
}
// Traverse tree starting at the provided sub-root.
int32 node_id = sub_root_id;
while (true) {
const auto& current_node = config.nodes(node_id);
switch (current_node.node_case()) {
case TreeNode::kLeaf: {
return node_id;
}
case TreeNode::kDenseFloatBinarySplit: {
const auto& split = current_node.dense_float_binary_split();
node_id = example.dense_float_features[split.feature_column()] <=
split.threshold()
? split.left_id()
: split.right_id();
break;
}
case TreeNode::kSparseFloatBinarySplitDefaultLeft: {
const auto& split =
current_node.sparse_float_binary_split_default_left().split();
auto sparse_feature =
example.sparse_float_features[split.feature_column()];
node_id = !sparse_feature.has_value() ||
sparse_feature.get_value() <= split.threshold()
? split.left_id()
: split.right_id();
break;
}
case TreeNode::kSparseFloatBinarySplitDefaultRight: {
const auto& split =
current_node.sparse_float_binary_split_default_right().split();
auto sparse_feature =
example.sparse_float_features[split.feature_column()];
node_id = sparse_feature.has_value() &&
sparse_feature.get_value() <= split.threshold()
? split.left_id()
: split.right_id();
break;
}
case TreeNode::kCategoricalIdBinarySplit: {
const auto& split = current_node.categorical_id_binary_split();
const auto& features =
example.sparse_int_features[split.feature_column()];
node_id = features.find(split.feature_id()) != features.end()
? split.left_id()
: split.right_id();
break;
}
case TreeNode::kCategoricalIdSetMembershipBinarySplit: {
const auto& split =
current_node.categorical_id_set_membership_binary_split();
// The new node_id = left_id if a feature is found, or right_id.
node_id = split.right_id();
for (const int64 feature_id :
example.sparse_int_features[split.feature_column()]) {
if (std::binary_search(split.feature_ids().begin(),
split.feature_ids().end(), feature_id)) {
node_id = split.left_id();
break;
}
}
break;
}
case TreeNode::NODE_NOT_SET: {
QCHECK(false) << "Invalid node in tree: " << current_node.DebugString();
break;
}
}
DCHECK_NE(node_id, 0) << "Malformed tree, cycles found to root:"
<< current_node.DebugString();
}
}
void DecisionTree::LinkChildren(const std::vector<int32>& children,
TreeNode* parent_node) {
// Decide how to link children depending on the parent node's type.
auto children_it = children.begin();
switch (parent_node->node_case()) {
case TreeNode::kLeaf: {
// Essentially no-op.
QCHECK(children.empty()) << "A leaf node cannot have children.";
break;
}
case TreeNode::kDenseFloatBinarySplit: {
QCHECK(children.size() == 2)
<< "A binary split node must have exactly two children.";
auto* split = parent_node->mutable_dense_float_binary_split();
split->set_left_id(*children_it);
split->set_right_id(*++children_it);
break;
}
case TreeNode::kSparseFloatBinarySplitDefaultLeft: {
QCHECK(children.size() == 2)
<< "A binary split node must have exactly two children.";
auto* split =
parent_node->mutable_sparse_float_binary_split_default_left()
->mutable_split();
split->set_left_id(*children_it);
split->set_right_id(*++children_it);
break;
}
case TreeNode::kSparseFloatBinarySplitDefaultRight: {
QCHECK(children.size() == 2)
<< "A binary split node must have exactly two children.";
auto* split =
parent_node->mutable_sparse_float_binary_split_default_right()
->mutable_split();
split->set_left_id(*children_it);
split->set_right_id(*++children_it);
break;
}
case TreeNode::kCategoricalIdBinarySplit: {
QCHECK(children.size() == 2)
<< "A binary split node must have exactly two children.";
auto* split = parent_node->mutable_categorical_id_binary_split();
split->set_left_id(*children_it);
split->set_right_id(*++children_it);
break;
}
case TreeNode::kCategoricalIdSetMembershipBinarySplit: {
QCHECK(children.size() == 2)
<< "A binary split node must have exactly two children.";
auto* split =
parent_node->mutable_categorical_id_set_membership_binary_split();
split->set_left_id(*children_it);
split->set_right_id(*++children_it);
break;
}
case TreeNode::NODE_NOT_SET: {
QCHECK(false) << "A non-set node cannot have children.";
break;
}
}
}
std::vector<int32> DecisionTree::GetChildren(const TreeNode& node) {
// A node's children depend on its type.
switch (node.node_case()) {
case TreeNode::kLeaf: {
return {};
}
case TreeNode::kDenseFloatBinarySplit: {
const auto& split = node.dense_float_binary_split();
return {split.left_id(), split.right_id()};
}
case TreeNode::kSparseFloatBinarySplitDefaultLeft: {
const auto& split = node.sparse_float_binary_split_default_left().split();
return {split.left_id(), split.right_id()};
}
case TreeNode::kSparseFloatBinarySplitDefaultRight: {
const auto& split =
node.sparse_float_binary_split_default_right().split();
return {split.left_id(), split.right_id()};
}
case TreeNode::kCategoricalIdBinarySplit: {
const auto& split = node.categorical_id_binary_split();
return {split.left_id(), split.right_id()};
}
case TreeNode::kCategoricalIdSetMembershipBinarySplit: {
const auto& split = node.categorical_id_set_membership_binary_split();
return {split.left_id(), split.right_id()};
}
case TreeNode::NODE_NOT_SET: {
return {};
}
}
}
} // namespace trees
} // namespace boosted_trees
} // namespace tensorflow
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