This file is intended to be notes about code structure in xgboost
- Dependency order: io->learner->gbm->tree
- All module depends on data.h
- tree are implementations of tree construction algorithms.
- gbm is gradient boosting interface, that takes trees and other base learner to do boosting.
- gbm only takes gradient as sufficient statistics, it does not compute the gradient.
- learner is learning module that computes gradient for specific object, and pass it to GBM
- .h files are data structures and interface, which are needed to use functions in that layer.
- -inl.hpp files are implementations of interface, like cpp file in most project.
- You only need to understand the interface file to understand the usage of that layer
- In each folder, there can be a .cpp file, that compiles the module of that layer
- Add objective function: add to learner/objective-inl.hpp and register it in learner/objective.h
CreateObjFunction- You can also directly do it in python
- Add new evaluation metric: add to learner/evaluation-inl.hpp and register it in learner/evaluation.h
CreateEvaluator - Add wrapper for a new language, most likely you can do it by taking the functions in python/xgboost_wrapper.h, which is purely C based, and call these C functions to use xgboost