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rank pass toy

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This commit is contained in:
kalenhaha
2014-04-07 23:25:35 +08:00
parent 40c380e40a
commit a10f594644
32 changed files with 2237 additions and 2146 deletions
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349
booster/xgboost_gbmbase.h
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@@ -8,25 +8,25 @@
#include "../utils/xgboost_config.h"
/*!
* \file xgboost_gbmbase.h
* \brief a base model class,
* \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
* this class can be used as base code to create booster variants
*
* The detailed implementation of boosters should start by using the class
* provided by this file
*
*
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
namespace xgboost{
namespace booster{
/*!
* \brief a base model class,
* \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
* this class can be used as base code to create booster variants
* *
* relation to xgboost.h:
* (1) xgboost.h provides a interface to a single booster(e.g. a single regression tree )
* while GBMBaseModel builds upon IBooster to build a class that
* while GBMBaseModel builds upon IBooster to build a class that
* ensembls the boosters together;
* (2) GBMBaseModel provides prediction buffering scheme to speedup training;
* (3) Summary: GBMBaseModel is a standard wrapper for boosting ensembles;
@@ -37,259 +37,260 @@ namespace xgboost{
* (3) model.InitTrainer before calling model.Predict and model.DoBoost
* (4) model.Predict to get predictions given a instance
* (4) model.DoBoost to update the ensembles, add new booster to the model
* (4) model.SaveModel to save learned results
* (4) model.SaveModel to save learned results
*
* Bufferring: each instance comes with a buffer_index in Predict.
* when mparam.num_pbuffer != 0, a unique buffer index can be
* Bufferring: each instance comes with a buffer_index in Predict.
* when mparam.num_pbuffer != 0, a unique buffer index can be
* assigned to each instance to buffer previous results of boosters,
* this helps to speedup training, so consider assign buffer_index
* this helps to speedup training, so consider assign buffer_index
* for each training instances, if buffer_index = -1, the code
* recalculate things from scratch and will still works correctly
*/
class GBMBase{
public:
/*! \brief number of thread used */
GBMBase( void ){}
GBMBase(void){}
/*! \brief destructor */
virtual ~GBMBase( void ){
virtual ~GBMBase(void){
this->FreeSpace();
}
/*!
* \brief set parameters from outside
/*!
* \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 ) ){
cfg.PushBack( name + 4, val );
inline void SetParam(const char *name, const char *val){
if (!strncmp(name, "bst:", 4)){
cfg.PushBack(name + 4, val);
}
if( !strcmp( name, "silent") ){
cfg.PushBack( name, val );
if (!strcmp(name, "silent")){
cfg.PushBack(name, val);
}
tparam.SetParam( name, val );
if( boosters.size() == 0 ) mparam.SetParam( name, val );
tparam.SetParam(name, val);
if (boosters.size() == 0) mparam.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( &mparam, sizeof(ModelParam) ) != 0 );
boosters.resize( mparam.num_boosters );
for( size_t i = 0; i < boosters.size(); i ++ ){
boosters[ i ] = booster::CreateBooster<FMatrixS>( mparam.booster_type );
boosters[ i ]->LoadModel( fi );
inline void LoadModel(utils::IStream &fi){
if (boosters.size() != 0) this->FreeSpace();
utils::Assert(fi.Read(&mparam, sizeof(ModelParam)) != 0);
boosters.resize(mparam.num_boosters);
for (size_t i = 0; i < boosters.size(); i++){
boosters[i] = booster::CreateBooster<FMatrixS>(mparam.booster_type);
boosters[i]->LoadModel(fi);
}
{// load info
booster_info.resize( mparam.num_boosters );
if( mparam.num_boosters != 0 ){
utils::Assert( fi.Read( &booster_info[0], sizeof(int)*mparam.num_boosters ) != 0 );
booster_info.resize(mparam.num_boosters);
if (mparam.num_boosters != 0){
utils::Assert(fi.Read(&booster_info[0], sizeof(int)*mparam.num_boosters) != 0);
}
}
if( mparam.num_pbuffer != 0 ){
pred_buffer.resize ( mparam.num_pbuffer );
pred_counter.resize( mparam.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 );
if (mparam.num_pbuffer != 0){
pred_buffer.resize(mparam.num_pbuffer);
pred_counter.resize(mparam.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( mparam.num_boosters == (int)boosters.size() );
fo.Write( &mparam, sizeof(ModelParam) );
for( size_t i = 0; i < boosters.size(); i ++ ){
boosters[ i ]->SaveModel( fo );
inline void SaveModel(utils::IStream &fo) const {
utils::Assert(mparam.num_boosters == (int)boosters.size());
fo.Write(&mparam, sizeof(ModelParam));
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 (booster_info.size() != 0){
fo.Write(&booster_info[0], sizeof(int)* booster_info.size());
}
if( mparam.num_pbuffer != 0 ){
fo.Write( &pred_buffer[0] , pred_buffer.size()*sizeof(float) );
fo.Write( &pred_counter[0], pred_counter.size()*sizeof(unsigned) );
if (mparam.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 ){
inline void InitModel(void){
pred_buffer.clear(); pred_counter.clear();
pred_buffer.resize ( mparam.num_pbuffer, 0.0 );
pred_counter.resize( mparam.num_pbuffer, 0 );
utils::Assert( mparam.num_boosters == 0 );
utils::Assert( boosters.size() == 0 );
pred_buffer.resize(mparam.num_pbuffer, 0.0);
pred_counter.resize(mparam.num_pbuffer, 0);
utils::Assert(mparam.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 ){
if( tparam.nthread != 0 ){
omp_set_num_threads( tparam.nthread );
* this function is reserved for solver to allocate necessary space and do other preparation
*/
inline void InitTrainer(void){
if (tparam.nthread != 0){
omp_set_num_threads(tparam.nthread);
}
// make sure all the boosters get the latest parameters
for( size_t i = 0; i < this->boosters.size(); i ++ ){
this->ConfigBooster( this->boosters[i] );
for (size_t i = 0; i < this->boosters.size(); i++){
this->ConfigBooster(this->boosters[i]);
}
}
/*!
/*!
* \brief DumpModel
* \param fo text file
* \param fo text file
* \param fmap feature map that may help give interpretations of feature
* \param with_stats whether print statistics
*/
inline void DumpModel( FILE *fo, const utils::FeatMap& fmap, bool with_stats ){
for( size_t i = 0; i < boosters.size(); i ++ ){
fprintf( fo, "booster[%d]\n", (int)i );
boosters[i]->DumpModel( fo, fmap, with_stats );
*/
inline void DumpModel(FILE *fo, const utils::FeatMap& fmap, bool with_stats){
for (size_t i = 0; i < boosters.size(); i++){
fprintf(fo, "booster[%d]\n", (int)i);
boosters[i]->DumpModel(fo, fmap, with_stats);
}
}
/*!
/*!
* \brief Dump path of all trees
* \param fo text file
* \param fo text file
* \param data input data
*/
inline void DumpPath( FILE *fo, const FMatrixS &data ){
for( size_t i = 0; i < data.NumRow(); ++ i ){
for( size_t j = 0; j < boosters.size(); ++ j ){
if( j != 0 ) fprintf( fo, "\t" );
inline void DumpPath(FILE *fo, const FMatrixS &data){
for (size_t i = 0; i < data.NumRow(); ++i){
for (size_t j = 0; j < boosters.size(); ++j){
if (j != 0) fprintf(fo, "\t");
std::vector<int> path;
boosters[j]->PredPath( path, data, i );
fprintf( fo, "%d", path[0] );
for( size_t k = 1; k < path.size(); ++ k ){
fprintf( fo, ",%d", path[k] );
boosters[j]->PredPath(path, data, i);
fprintf(fo, "%d", path[0]);
for (size_t k = 1; k < path.size(); ++k){
fprintf(fo, ",%d", path[k]);
}
}
fprintf( fo, "\n" );
fprintf(fo, "\n");
}
}
public:
/*!
/*!
* \brief do gradient boost training for one step, using the information given
* Note: content of grad and hess can change after DoBoost
* \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,
* \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 &feats,
const std::vector<unsigned> &root_index ) {
inline void DoBoost(std::vector<float> &grad,
std::vector<float> &hess,
const booster::FMatrixS &feats,
const std::vector<unsigned> &root_index) {
booster::IBooster *bst = this->GetUpdateBooster();
bst->DoBoost( grad, hess, feats, root_index );
bst->DoBoost(grad, hess, feats, root_index);
}
/*!
/*!
* \brief predict values for given sparse feature vector
* NOTE: in tree implementation, this is only OpenMP threadsafe, but not threadsafe
* \param feats feature matrix
* \param row_index row index in the feature matrix
* \param buffer_index the buffer index of the current feature line, default -1 means no buffer assigned
* \param root_index root id of current instance, default = 0
* \return prediction
* \return prediction
*/
inline float Predict( const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0 ){
inline float Predict(const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0){
size_t istart = 0;
float psum = 0.0f;
// load buffered results if any
if( mparam.do_reboost == 0 && buffer_index >= 0 ){
utils::Assert( buffer_index < mparam.num_pbuffer, "buffer index exceed num_pbuffer" );
istart = this->pred_counter[ buffer_index ];
psum = this->pred_buffer [ buffer_index ];
if (mparam.do_reboost == 0 && buffer_index >= 0){
utils::Assert(buffer_index < mparam.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(feats, row_index, root_index);
}
for( size_t i = istart; i < this->boosters.size(); i ++ ){
psum += this->boosters[ i ]->Predict( feats, row_index, root_index );
}
// updated the buffered results
if( mparam.do_reboost == 0 && buffer_index >= 0 ){
this->pred_counter[ buffer_index ] = static_cast<unsigned>( boosters.size() );
this->pred_buffer [ buffer_index ] = psum;
if (mparam.do_reboost == 0 && buffer_index >= 0){
this->pred_counter[buffer_index] = static_cast<unsigned>(boosters.size());
this->pred_buffer[buffer_index] = psum;
}
return psum;
}
public:
//--------trial code for interactive update an existing booster------
//-------- usually not needed, ignore this region ---------
/*!
* \brief same as Predict, but removes the prediction of booster to be updated
/*!
* \brief same as Predict, but removes the prediction of booster to be updated
* this function must be called once and only once for every data with pbuffer
*/
inline float InteractPredict( const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0 ){
float psum = this->Predict( feats, row_index, buffer_index, root_index );
if( tparam.reupdate_booster != -1 ){
inline float InteractPredict(const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0){
float psum = this->Predict(feats, row_index, buffer_index, root_index);
if (tparam.reupdate_booster != -1){
const int bid = tparam.reupdate_booster;
utils::Assert( bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound" );
psum -= boosters[ bid ]->Predict( feats, row_index, root_index );
if( mparam.do_reboost == 0 && buffer_index >= 0 ){
this->pred_buffer[ buffer_index ] = psum;
utils::Assert(bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound");
psum -= boosters[bid]->Predict(feats, row_index, root_index);
if (mparam.do_reboost == 0 && buffer_index >= 0){
this->pred_buffer[buffer_index] = psum;
}
}
return psum;
}
/*! \brief delete the specified booster */
inline void DelteBooster( void ){
inline void DelteBooster(void){
const int bid = tparam.reupdate_booster;
utils::Assert( bid >= 0 && bid < mparam.num_boosters , "must specify booster index for deletion");
delete boosters[ bid ];
for( int i = bid + 1; i < mparam.num_boosters; ++ i ){
boosters[i-1] = boosters[ i ];
booster_info[i-1] = booster_info[ i ];
}
boosters.resize( mparam.num_boosters -= 1 );
booster_info.resize( boosters.size() );
utils::Assert(bid >= 0 && bid < mparam.num_boosters, "must specify booster index for deletion");
delete boosters[bid];
for (int i = bid + 1; i < mparam.num_boosters; ++i){
boosters[i - 1] = boosters[i];
booster_info[i - 1] = booster_info[i];
}
boosters.resize(mparam.num_boosters -= 1);
booster_info.resize(boosters.size());
}
/*! \brief update the prediction buffer, after booster have been updated */
inline void InteractRePredict( const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0 ){
if( tparam.reupdate_booster != -1 ){
/*! \brief update the prediction buffer, after booster have been updated */
inline void InteractRePredict(const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0){
if (tparam.reupdate_booster != -1){
const int bid = tparam.reupdate_booster;
utils::Assert( bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound" );
if( mparam.do_reboost == 0 && buffer_index >= 0 ){
this->pred_buffer[ buffer_index ] += boosters[ bid ]->Predict( feats, row_index, root_index );
utils::Assert(bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound");
if (mparam.do_reboost == 0 && buffer_index >= 0){
this->pred_buffer[buffer_index] += boosters[bid]->Predict(feats, row_index, root_index);
}
}
}
//-----------non public fields afterwards-------------
protected:
/*! \brief free space of the model */
inline void FreeSpace( void ){
for( size_t i = 0; i < boosters.size(); i ++ ){
inline void FreeSpace(void){
for (size_t i = 0; i < boosters.size(); i++){
delete boosters[i];
}
boosters.clear(); booster_info.clear(); mparam.num_boosters = 0;
boosters.clear(); booster_info.clear(); mparam.num_boosters = 0;
}
/*! \brief configure a booster */
inline void ConfigBooster( booster::IBooster *bst ){
inline void ConfigBooster(booster::IBooster *bst){
cfg.BeforeFirst();
while( cfg.Next() ){
bst->SetParam( cfg.name(), cfg.val() );
while (cfg.Next()){
bst->SetParam(cfg.name(), cfg.val());
}
}
/*!
* \brief get a booster to update
/*!
* \brief get a booster to update
* \return the booster created
*/
inline booster::IBooster *GetUpdateBooster( void ){
if( tparam.reupdate_booster != -1 ){
inline booster::IBooster *GetUpdateBooster(void){
if (tparam.reupdate_booster != -1){
const int bid = tparam.reupdate_booster;
utils::Assert( bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound" );
this->ConfigBooster( boosters[bid] );
return boosters[ bid ];
utils::Assert(bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound");
this->ConfigBooster(boosters[bid]);
return boosters[bid];
}
if( mparam.do_reboost == 0 || boosters.size() == 0 ){
if (mparam.do_reboost == 0 || boosters.size() == 0){
mparam.num_boosters += 1;
boosters.push_back( booster::CreateBooster<FMatrixS>( mparam.booster_type ) );
booster_info.push_back( 0 );
this->ConfigBooster( boosters.back() );
boosters.back()->InitModel();
}else{
this->ConfigBooster( boosters.back() );
boosters.push_back(booster::CreateBooster<FMatrixS>(mparam.booster_type));
booster_info.push_back(0);
this->ConfigBooster(boosters.back());
boosters.back()->InitModel();
}
else{
this->ConfigBooster(boosters.back());
}
return boosters.back();
}
@@ -306,76 +307,76 @@ namespace xgboost{
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 ];
int reserved[32];
/*! \brief constructor */
ModelParam( void ){
num_boosters = 0;
ModelParam(void){
num_boosters = 0;
booster_type = 0;
num_roots = num_feature = 0;
num_roots = num_feature = 0;
do_reboost = 0;
num_pbuffer = 0;
memset( reserved, 0, sizeof( reserved ) );
memset(reserved, 0, sizeof(reserved));
}
/*!
* \brief set parameters from outside
/*!
* \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 );
inline void SetParam(const char *name, const char *val){
if (!strcmp("booster_type", name)){
booster_type = atoi(val);
// linear boost automatically set do reboost
if( booster_type == 1 ) do_reboost = 1;
if (booster_type == 1) do_reboost = 1;
}
if( !strcmp("num_pbuffer", name ) ) num_pbuffer = atoi( val );
if( !strcmp("do_reboost", name ) ) do_reboost = atoi( val );
if( !strcmp("bst:num_roots", name ) ) num_roots = atoi( val );
if( !strcmp("bst:num_feature", name ) ) num_feature = atoi( val );
if (!strcmp("num_pbuffer", name)) num_pbuffer = atoi(val);
if (!strcmp("do_reboost", name)) do_reboost = atoi(val);
if (!strcmp("bst:num_roots", name)) num_roots = atoi(val);
if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
}
};
/*! \brief training parameters */
struct TrainParam{
/*! \brief number of OpenMP threads */
int nthread;
/*!
* \brief index of specific booster to be re-updated, default = -1: update new booster
/*!
* \brief index of specific booster to be re-updated, default = -1: update new booster
* parameter this is part of trial interactive update mode
*/
int reupdate_booster;
/*! \brief constructor */
TrainParam( void ) {
TrainParam(void) {
nthread = 1;
reupdate_booster = -1;
}
/*!
* \brief set parameters from outside
/*!
* \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("nthread", name ) ) nthread = atoi( val );
if( !strcmp("interact:booster_index", name ) ) reupdate_booster = atoi( val );
*/
inline void SetParam(const char *name, const char *val){
if (!strcmp("nthread", name)) nthread = atoi(val);
if (!strcmp("interact:booster_index", name)) reupdate_booster = atoi(val);
}
};
protected:
/*! \brief model parameters */
/*! \brief model parameters */
ModelParam mparam;
/*! \brief training parameters */
/*! \brief training parameters */
TrainParam tparam;
protected:
/*! \brief component boosters */
/*! \brief component boosters */
std::vector<booster::IBooster*> boosters;
/*! \brief some information indicator of the booster, reserved */
/*! \brief some information indicator of the booster, reserved */
std::vector<int> booster_info;
/*! \brief prediction buffer */
/*! \brief prediction buffer */
std::vector<float> pred_buffer;
/*! \brief prediction buffer counter, record the progress so fart of the 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;