Development Guide

Algorithms

Refer to Features for understanding of important algorithms used in LightGBM.

Classes and Code Structure

Important Classes

Class

Description

Application

The entrance of application, including training and prediction logic

Bin

Data structure used for storing feature discrete values (converted from float values)

Boosting

Boosting interface (GBDT, DART, GOSS, etc.)

Config

Stores parameters and configurations

Dataset

Stores information of dataset

DatasetLoader

Used to construct dataset

FeatureGroup

Stores the data of feature, could be multiple features

Metric

Evaluation metrics

Network

Network interfaces and communication algorithms

ObjectiveFunction

Objective functions used to train

Tree

Stores information of tree model

TreeLearner

Used to learn trees

Code Structure

Path

Description

./include

Header files

./include/utils

Some common functions

./src/application

Implementations of training and prediction logic

./src/boosting

Implementations of Boosting

./src/io

Implementations of IO related classes, including Bin, Config, Dataset, DatasetLoader, Feature and Tree

./src/metric

Implementations of metrics

./src/network

Implementations of network functions

./src/objective

Implementations of objective functions

./src/treelearner

Implementations of tree learners

Documents API

Refer to docs README.

C API

Refer to C API or the comments in c_api.h file, from which the documentation is generated.

Tests

C++ unit tests are located in the ./tests/cpp_tests folder and written with the help of Google Test framework. To run tests locally first refer to the Installation Guide for how to build tests and then simply run compiled executable file.

High Level Language Package

See the implementations at Python-package and R-package.

Questions

Refer to FAQ.

Also feel free to open issues if you met problems.