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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.
// =============================================================================
#ifndef TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_TREE_RESOURCE_H_
#define TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_TREE_RESOURCE_H_
#include "tensorflow/contrib/decision_trees/proto/generic_tree_model.pb.h"
#include "tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h"
#include "tensorflow/contrib/tensor_forest/kernels/v4/input_data.h"
#include "tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.h"
#include "tensorflow/contrib/tensor_forest/proto/fertile_stats.pb.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/platform/mutex.h"
namespace tensorflow {
namespace tensorforest {
// Keep a tree ensemble in memory for efficient evaluation and mutation.
class DecisionTreeResource : public ResourceBase {
public:
// Constructor.
explicit DecisionTreeResource(const TensorForestParams& params);
string DebugString() override {
return strings::StrCat("DecisionTree[size=",
decision_tree_->decision_tree().nodes_size(), "]");
}
void MaybeInitialize();
const decision_trees::Model& decision_tree() const { return *decision_tree_; }
decision_trees::Model* mutable_decision_tree() {
return decision_tree_.get();
}
const decision_trees::Leaf& get_leaf(int32 id) const {
return decision_tree_->decision_tree().nodes(id).leaf();
}
decision_trees::TreeNode* get_mutable_tree_node(int32 id) {
return decision_tree_->mutable_decision_tree()->mutable_nodes(id);
}
// Resets the resource and frees the proto.
// Caller needs to hold the mutex lock while calling this.
void Reset() { decision_tree_.reset(new decision_trees::Model()); }
mutex* get_mutex() { return &mu_; }
// Return the TreeNode for the leaf that the example ends up at according
// to decision_tree_. Also fill in that leaf's depth if it isn't nullptr.
int32 TraverseTree(const std::unique_ptr<TensorDataSet>& input_data,
int example, int32* depth, TreePath* path) const;
// Split the given node_id, turning it from a Leaf to a BinaryNode and
// setting it's split to the given best. Add new children ids to
// new_children.
void SplitNode(int32 node_id, SplitCandidate* best,
std::vector<int32>* new_children);
private:
mutex mu_;
const TensorForestParams params_;
std::unique_ptr<decision_trees::Model> decision_tree_;
std::shared_ptr<LeafModelOperator> model_op_;
std::vector<std::unique_ptr<DecisionNodeEvaluator>> node_evaluators_;
};
} // namespace tensorforest
} // namespace tensorflow
#endif // TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_DECISION_TREE_RESOURCE_H_
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