100 lines
4.0 KiB
C++
100 lines
4.0 KiB
C++
#ifndef _XGBOOST_H_
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#define _XGBOOST_H_
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/*!
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* \file xgboost.h
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* \brief the general gradient boosting interface
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* \author Tianqi Chen: tianqi.tchen@gmail.com
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*/
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#include <vector>
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#include "../utils/xgboost_utils.h"
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#include "../utils/xgboost_stream.h"
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#include "xgboost_data.h"
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/*! \brief namespace for xboost package */
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namespace xgboost{
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/*! \brief namespace for boosters */
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namespace booster{
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/*! \brief interface of a gradient boosting learner */
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class IBooster{
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public:
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// interface for model setting and loading
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// calling procedure:
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// (1) booster->SetParam to setting necessary parameters
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// (2) if it is first time usage of the model:
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// call booster->InitModel
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// else:
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// call booster->LoadModel
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// (3) booster->DoBoost to update the model
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// (4) booster->Predict to get new prediction
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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virtual void SetParam( const char *name, const char *val ) = 0;
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/*!
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* \brief load model from stream
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* \param fi input stream
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*/
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virtual void LoadModel( utils::IStream &fi ) = 0;
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/*!
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* \brief save model to stream
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* \param fo output stream
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*/
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virtual void SaveModel( utils::IStream &fo ) const = 0;
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/*!
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* \brief initialize solver before training, called before training
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* this function is reserved for solver to allocate necessary space and do other preparations
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*/
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virtual void InitModel( void ) = 0;
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public:
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/*!
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* \brief do gradient boost training for one step, using the information given
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* \param grad first order gradient of each instance
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* \param hess second order gradient of each instance
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* \param feats features of each instance
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* \param root_index pre-partitioned root index of each instance,
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* root_index.size() can be 0 which indicates that no pre-partition involved
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*/
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virtual void DoBoost( std::vector<float> &grad,
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std::vector<float> &hess,
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const FMatrixS::Image &feats,
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const std::vector<unsigned> &root_index ) = 0;
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/*!
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* \brief predict values for given sparse feature vector
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* NOTE: in tree implementation, this is not threadsafe
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* \param feat vector in sparse format
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* \param rid root id of current instance, default = 0
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* \return prediction
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*/
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virtual float Predict( const FMatrixS::Line &feat, unsigned rid = 0 ){
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utils::Error( "not implemented" );
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return 0.0f;
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}
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/*!
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* \brief predict values for given dense feature vector
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* \param feat feature vector in dense format
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* \param funknown indicator that the feature is missing
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* \param rid root id of current instance, default = 0
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* \return prediction
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*/
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virtual float Predict( const std::vector<float> &feat,
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const std::vector<bool> &funknown,
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unsigned rid = 0 ){
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utils::Error( "not implemented" );
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return 0.0f;
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}
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/*!
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* \brief print information
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* \param fo output stream
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*/
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virtual void PrintInfo( FILE *fo ){}
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public:
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/*! \brief virtual destructor */
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virtual ~IBooster( void ){}
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};
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};
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};
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#endif
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