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.