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add base code

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tqchen 2014-02-07 18:40:53 -08:00
parent 4e2d67b81a
commit 0d3ecd9033
3 changed files with 286 additions and 1 deletions
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14
booster/xgboost.cpp
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@ -9,5 +9,19 @@
#include <climits> #include <climits>
#include "xgboost.h" #include "xgboost.h"
#include "../utils/xgboost_utils.h" #include "../utils/xgboost_utils.h"
#include "../gbm-base/xgboost_base_model.h"
namespace xgboost{
namespace booster{
/*!
* \brief create a gradient booster, given type of booster
* \param booster_type type of gradient booster, can be used to specify implements
* \return the pointer to the gradient booster created
*/
IBooster *CreateBooster( int booster_type ){
// TODO
return NULL;
}
};
};

2
booster/xgboost.h
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@ -104,7 +104,7 @@ namespace xgboost{
* \param booster_type type of gradient booster, can be used to specify implements * \param booster_type type of gradient booster, can be used to specify implements
* \return the pointer to the gradient booster created * \return the pointer to the gradient booster created
*/ */
IBooster *create_booster( int booster_type ); IBooster *CreateBooster( int booster_type );
}; };
}; };
#endif #endif

271
gbm-base/xgboost_base_model.h Normal file
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@ -0,0 +1,271 @@
#ifndef _XGBOOST_BASE_MODEL_H_
#define _XGBOOST_BASE_MODEL_H_
#include <cstring>
#include "../booster/xgboost.h"
#include "../utils/xgboost_config.h"
/*!
* \file xgboost_base_model.h
* \brief a base model class,
* that assembles the ensembles of booster together and do model update
* this class can be used as base code to create booster variants
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
namespace xgboost{
/*! \brief namespace for base class library */
namespace gbm_base{
/*!
* \brief a base model class,
* that assembles the ensembles of booster together and provide single routines to do prediction buffer and update
* this class can be used as base code to create booster variants
*/
class BaseGBMModel{
public:
/*! \brief model parameters */
struct Param{
/*! \brief number of boosters */
int num_boosters;
/*! \brief type of tree used */
int booster_type;
/*! \brief number of root: default 0, means single tree */
int num_root;
/*! \brief number of features to be used by boosters */
int num_feature;
/*! \brief size of predicton buffer allocated for buffering boosting computation */
int num_pbuffer;
/*!
* \brief whether we repeatly update a single booster each round: default 0
* set to 1 for linear booster, so that regularization term can be considered
*/
int do_reboost;
/*! \brief reserved parameters */
int reserved[ 32 ];
/*! \brief constructor */
Param( void ){
num_boosters = 0;
booster_type = 0;
num_root = num_feature = 0;
do_reboost = 0;
num_pbuffer = 0;
memset( reserved, 0, sizeof( reserved ) );
}
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
inline void SetParam( const char *name, const char *val ){
if( !strcmp("booster_type", name ) ) booster_type = atoi( val );
if( !strcmp("num_pbuffer", name ) ) num_pbuffer = atoi( val );
if( !strcmp("do_reboost", name ) ) do_reboost = atoi( val );
if( !strcmp("bst:num_root", name ) ) num_root = atoi( val );
if( !strcmp("bst:num_feature", name ) ) num_feature = atoi( val );
}
};
public:
/*! \brief destructor */
virtual ~BaseGBMModel( void ){
this->FreeSpace();
}
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
inline void SetParam( const char *name, const char *val ){
if( strncmp( name, "bst:", 4 ) == 0 ){
cfg.PushBack( name + 4, val );
}
if( boosters.size() == 0 ) param.SetParam( name, val );
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void LoadModel( utils::IStream &fi ){
if( boosters.size() != 0 ) this->FreeSpace();
utils::Assert( fi.Read( &param, sizeof(Param) ) != 0 );
boosters.resize( param.num_boosters );
for( size_t i = 0; i < boosters.size(); i ++ ){
boosters[ i ] = booster::CreateBooster( param.booster_type );
boosters[ i ]->LoadModel( fi );
}
{// load info
booster_info.resize( param.num_boosters );
if( param.num_boosters != 0 ){
utils::Assert( fi.Read( &booster_info[0], sizeof(int)*param.num_boosters ) != 0 );
}
}
if( param.num_pbuffer != 0 ){
pred_buffer.resize ( param.num_pbuffer );
pred_counter.resize( param.num_pbuffer );
utils::Assert( fi.Read( &pred_buffer[0] , pred_buffer.size()*sizeof(float) ) != 0 );
utils::Assert( fi.Read( &pred_counter[0], pred_counter.size()*sizeof(unsigned) ) != 0 );
}
}
/*!
* \brief save model to stream
* \param fo output stream
*/
inline void SaveModel( utils::IStream &fo ) const {
utils::Assert( param.num_boosters == (int)boosters.size() );
fo.Write( &param, sizeof(Param) );
for( size_t i = 0; i < boosters.size(); i ++ ){
boosters[ i ]->SaveModel( fo );
}
if( booster_info.size() != 0 ){
fo.Write( &booster_info[0], sizeof(int) * booster_info.size() );
}
if( param.num_pbuffer != 0 ){
fo.Write( &pred_buffer[0] , pred_buffer.size()*sizeof(float) );
fo.Write( &pred_counter[0], pred_counter.size()*sizeof(unsigned) );
}
}
/*!
* \brief initialize the current data storage for model, if the model is used first time, call this function
*/
inline void InitModel( void ){
pred_buffer.clear(); pred_counter.clear();
pred_buffer.resize ( param.num_pbuffer, 0.0 );
pred_counter.resize( param.num_pbuffer, 0 );
utils::Assert( param.num_boosters == 0 );
utils::Assert( boosters.size() == 0 );
}
/*!
* \brief initialize solver before training, called before training
* this function is reserved for solver to allocate necessary space and do other preparation
*/
inline void InitTrainer( void ){
// make sure all the boosters get the latest parameters
for( size_t i = 0; i < this->boosters.size(); i ++ ){
this->ConfigBooster( this->boosters[i] );
}
}
public:
/*!
* \brief do gradient boost training for one step, using the information given
* \param grad first order gradient of each instance
* \param hess second order gradient of each instance
* \param feats features of each instance
* \param root_index pre-partitioned root index of each instance,
* root_index.size() can be 0 which indicates that no pre-partition involved
*/
inline void DoBoost( std::vector<float> &grad,
std::vector<float> &hess,
const booster::FMatrixS::Image &feats,
const std::vector<unsigned> &root_index ) {
booster::IBooster *bst = this->GetUpdateBooster();
bst->DoBoost( grad, hess, feats, root_index );
}
/*!
* \brief predict values for given sparse feature vector
* NOTE: in tree implementation, this is not threadsafe
* \param feat vector in sparse format
* \param buffer_index the buffer index of the current feature line, default -1 means no buffer assigned
* \param rid root id of current instance, default = 0
* \return prediction
*/
virtual float Predict( const booster::FMatrixS::Line &feat, int buffer_index = -1, unsigned rid = 0 ){
size_t istart = 0;
float psum = 0.0f;
// load buffered results if any
if( param.do_reboost == 0 && buffer_index >= 0 ){
utils::Assert( buffer_index < param.num_pbuffer, "buffer index exceed num_pbuffer" );
istart = this->pred_counter[ buffer_index ];
psum = this->pred_buffer [ buffer_index ];
}
for( size_t i = istart; i < this->boosters.size(); i ++ ){
psum += this->boosters[ i ]->Predict( feat, rid );
}
// updated the buffered results
if( param.do_reboost == 0 && buffer_index >= 0 ){
this->pred_counter[ buffer_index ] = static_cast<unsigned>( boosters.size() );
this->pred_buffer [ buffer_index ] = psum;
}
return psum;
}
/*!
* \brief predict values for given dense feature vector
* \param feat feature vector in dense format
* \param funknown indicator that the feature is missing
* \param buffer_index the buffer index of the current feature line, default -1 means no buffer assigned
* \param rid root id of current instance, default = 0
* \return prediction
*/
virtual float Predict( const std::vector<float> &feat,
const std::vector<bool> &funknown,
int buffer_index = -1,
unsigned rid = 0 ){
size_t istart = 0;
float psum = 0.0f;
// load buffered results if any
if( param.do_reboost == 0 && buffer_index >= 0 ){
utils::Assert( buffer_index < param.num_pbuffer, "buffer index exceed num_pbuffer" );
istart = this->pred_counter[ buffer_index ];
psum = this->pred_buffer [ buffer_index ];
}
for( size_t i = istart; i < this->boosters.size(); i ++ ){
psum += this->boosters[ i ]->Predict( feat, funknown, rid );
}
// updated the buffered results
if( param.do_reboost == 0 && buffer_index >= 0 ){
this->pred_counter[ buffer_index ] = static_cast<unsigned>( boosters.size() );
this->pred_buffer [ buffer_index ] = psum;
}
return psum;
}
//-----------non public fields afterwards-------------
protected:
/*! \brief free space of the model */
inline void FreeSpace( void ){
for( size_t i = 0; i < boosters.size(); i ++ ){
delete boosters[i];
}
boosters.clear(); booster_info.clear(); param.num_boosters = 0;
}
/*! \brief configure a booster */
inline void ConfigBooster( booster::IBooster *bst ){
cfg.BeforeFirst();
while( cfg.Next() ){
bst->SetParam( cfg.name(), cfg.val() );
}
}
/*!
* \brief get a booster to update
* \return the booster created
*/
inline booster::IBooster *GetUpdateBooster( void ){
if( param.do_reboost == 0 || boosters.size() == 0 ){
boosters.push_back( booster::CreateBooster( param.booster_type ) );
booster_info.push_back( 0 );
this->ConfigBooster( boosters.back() );
boosters.back()->InitModel();
}else{
this->ConfigBooster( boosters.back() );
}
return boosters.back();
}
protected:
/*! \brief model parameters */
Param param;
/*! \brief component boosters */
std::vector<booster::IBooster*> boosters;
/*! \brief some information indicator of the booster, reserved */
std::vector<int> booster_info;
/*! \brief prediction buffer */
std::vector<float> pred_buffer;
/*! \brief prediction buffer counter, record the progress so fart of the buffer */
std::vector<unsigned> pred_counter;
/*! \brief configurations saved for each booster */
utils::ConfigSaver cfg;
};
};
};
#endif