wehub-resource-sync
120 lines
4.1 KiB
C++
120 lines
4.1 KiB
C++
/*!
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* Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.
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* Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.
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* Licensed under the MIT License. See LICENSE file in the project root for license information.
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*/
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#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_
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#define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_
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#include <LightGBM/config.h>
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#include <LightGBM/meta.h>
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#include <LightGBM/utils/json11.h>
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#include <string>
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#include <vector>
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namespace LightGBM {
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using json11_internal_lightgbm::Json;
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/*! \brief forward declaration */
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class Tree;
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class Dataset;
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class ObjectiveFunction;
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/*!
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* \brief Interface for tree learner
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*/
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class TreeLearner {
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public:
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/*! \brief virtual destructor */
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virtual ~TreeLearner() {}
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/*!
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* \brief Initialize tree learner with training dataset
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* \param train_data The used training data
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* \param is_constant_hessian True if all hessians share the same value
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*/
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virtual void Init(const Dataset* train_data, bool is_constant_hessian) = 0;
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/*! Initialise some temporary storage, only needed for the linear tree; needs to be a method of TreeLearner since we call it in GBDT::RefitTree */
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virtual void InitLinear(const Dataset* /*train_data*/, const int /*max_leaves*/) {}
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virtual void ResetIsConstantHessian(bool is_constant_hessian) = 0;
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virtual void ResetTrainingData(const Dataset* train_data,
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bool is_constant_hessian) = 0;
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/*!
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* \brief Reset tree configs
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* \param config config of tree
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*/
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virtual void ResetConfig(const Config* config) = 0;
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/*!
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* \brief Reset boosting_on_gpu_
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* \param boosting_on_gpu flag for boosting on GPU
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*/
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virtual void ResetBoostingOnGPU(const bool /*boosting_on_gpu*/) {}
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virtual void SetForcedSplit(const Json* forced_split_json) = 0;
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/*!
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* \brief training tree model on dataset
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* \param gradients The first order gradients
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* \param hessians The second order gradients
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* \param is_first_tree If linear tree learning is enabled, first tree needs to be handled differently
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* \return A trained tree
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*/
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virtual Tree* Train(const score_t* gradients, const score_t* hessians, bool is_first_tree) = 0;
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/*!
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* \brief use an existing tree to fit the new gradients and hessians.
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*/
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virtual Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const = 0;
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virtual Tree* FitByExistingTree(const Tree* old_tree, const std::vector<int>& leaf_pred,
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const score_t* gradients, const score_t* hessians) const = 0;
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/*!
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* \brief Set bagging data
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* \param subset subset of bagging
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* \param used_indices Used data indices
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* \param num_data Number of used data
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*/
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virtual void SetBaggingData(const Dataset* subset,
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const data_size_t* used_indices,
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data_size_t num_data) = 0;
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/*!
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* \brief Using last trained tree to predict score then adding to out_score;
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* \param out_score output score
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*/
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virtual void AddPredictionToScore(const Tree* tree, double* out_score) const = 0;
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virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function<double(const label_t*, int)> residual_getter,
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data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const = 0;
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TreeLearner() = default;
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/*! \brief Disable copy */
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TreeLearner& operator=(const TreeLearner&) = delete;
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/*! \brief Disable copy */
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TreeLearner(const TreeLearner&) = delete;
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/*!
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* \brief Create object of tree learner
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* \param learner_type Type of tree learner
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* \param device_type Type of tree learner
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* \param booster_type Type of boosting
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* \param config config of tree
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*/
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static TreeLearner* CreateTreeLearner(const std::string& learner_type,
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const std::string& device_type,
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const Config* config,
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const bool boosting_on_cuda);
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};
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} // namespace LightGBM
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#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_
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