Data parallel learning algorithm. Workers use local data to construct histograms locally, then sync up global histograms. It is recommonded used when data is large or #feature is small. More...

#include <parallel_tree_learner.h>

Inheritance diagram for LightGBM::DataParallelTreeLearner< TREELEARNER_T >:

Public Member Functions
	DataParallelTreeLearner (const Config *config)

void	Init (const Dataset *train_data, bool is_constant_hessian) override

void	ResetConfig (const Config *config) override

Protected Member Functions
void	BeforeTrain () override

void	FindBestSplits () override

void	FindBestSplitsFromHistograms (const std::vector< int8_t > &is_feature_used, bool use_subtract) override

void	Split (Tree tree, int best_Leaf, int left_leaf, int *right_leaf) override

data_size_t	GetGlobalDataCountInLeaf (int leaf_idx) const override

Detailed Description

template<typename TREELEARNER_T>
class LightGBM::DataParallelTreeLearner< TREELEARNER_T >

Data parallel learning algorithm. Workers use local data to construct histograms locally, then sync up global histograms. It is recommonded used when data is large or #feature is small.

The documentation for this class was generated from the following files:

External/LightGBM_2.2.3/LightGBM-2.2.3/src/treelearner/parallel_tree_learner.h
External/LightGBM_2.2.3/LightGBM-2.2.3/src/treelearner/data_parallel_tree_learner.cpp

Public Member Functions

Protected Member Functions

Detailed Description