wehub-resource-sync
89 lines
2.9 KiB
C++
89 lines
2.9 KiB
C++
/*!
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* Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.
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* Copyright (c) 2021-2026 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_SAMPLE_STRATEGY_H_
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#define LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_
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#include <LightGBM/cuda/cuda_utils.hu>
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#include <LightGBM/utils/random.h>
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#include <LightGBM/utils/common.h>
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#include <LightGBM/utils/threading.h>
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#include <LightGBM/config.h>
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#include <LightGBM/dataset.h>
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#include <LightGBM/tree_learner.h>
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#include <LightGBM/objective_function.h>
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#include <memory>
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#include <vector>
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namespace LightGBM {
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class SampleStrategy {
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public:
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SampleStrategy() : balanced_bagging_(false), bagging_runner_(0, bagging_rand_block_), need_resize_gradients_(false) {}
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virtual ~SampleStrategy() {}
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static SampleStrategy* CreateSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration);
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virtual void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) = 0;
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virtual void ResetSampleConfig(const Config* config, bool is_change_dataset) = 0;
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bool is_use_subset() const { return is_use_subset_; }
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data_size_t bag_data_cnt() const { return bag_data_cnt_; }
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std::vector<data_size_t, Common::AlignmentAllocator<data_size_t, kAlignedSize>>& bag_data_indices() { return bag_data_indices_; }
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#ifdef USE_CUDA
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CUDAVector<data_size_t>& cuda_bag_data_indices() { return cuda_bag_data_indices_; }
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#endif // USE_CUDA
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void UpdateObjectiveFunction(const ObjectiveFunction* objective_function) {
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objective_function_ = objective_function;
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}
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void UpdateTrainingData(const Dataset* train_data) {
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train_data_ = train_data;
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num_data_ = train_data->num_data();
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}
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virtual bool IsHessianChange() const = 0;
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bool NeedResizeGradients() const { return need_resize_gradients_; }
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virtual data_size_t num_sampled_queries() const { return 0; }
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virtual const data_size_t* sampled_query_indices() const { return nullptr; }
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protected:
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const Config* config_;
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const Dataset* train_data_;
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const ObjectiveFunction* objective_function_;
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std::vector<data_size_t, Common::AlignmentAllocator<data_size_t, kAlignedSize>> bag_data_indices_;
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data_size_t bag_data_cnt_;
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data_size_t num_data_;
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int num_tree_per_iteration_;
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std::unique_ptr<Dataset> tmp_subset_;
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bool is_use_subset_;
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bool balanced_bagging_;
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const int bagging_rand_block_ = 1024;
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std::vector<Random> bagging_rands_;
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ParallelPartitionRunner<data_size_t, false> bagging_runner_;
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/*! \brief whether need to resize the gradient vectors */
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bool need_resize_gradients_;
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#ifdef USE_CUDA
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/*! \brief Buffer for bag_data_indices_ on GPU, used only with cuda */
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CUDAVector<data_size_t> cuda_bag_data_indices_;
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#endif // USE_CUDA
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};
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} // namespace LightGBM
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#endif // LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_
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