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start unity refactor

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tqchen 2014-08-15 20:15:58 -07:00
parent 5b215742c2
commit 2a92c82b92
49 changed files with 3659 additions and 5803 deletions
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12
Makefile
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@ -1,16 +1,16 @@
export CC = gcc
export CXX = g++
export CFLAGS = -Wall -O3 -msse2 -Wno-unknown-pragmas -fopenmp
export CC = clang
export CXX = clang++
export CFLAGS = -Wall -O3 -msse2 -Wno-unknown-pragmas
# specify tensor path
BIN = xgboost
BIN = xgunity.exe
OBJ =
.PHONY: clean all
all: $(BIN) $(OBJ)
export LDFLAGS= -pthread -lm
xgboost: regrank/xgboost_regrank_main.cpp regrank/*.h regrank/*.hpp booster/*.h booster/*/*.hpp booster/*.hpp
xgunity.exe: xgunity.cpp
$(BIN) :
@ -23,4 +23,4 @@ install:
cp -f -r $(BIN) $(INSTALL_PATH)
clean:
$(RM) $(OBJ) $(BIN) *~
$(RM) $(OBJ) $(BIN) *~ */*~

45
README.md
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@ -8,33 +8,9 @@ Turorial and Documentation: https://github.com/tqchen/xgboost/wiki
Questions and Issues: [https://github.com/tqchen/xgboost/issues](https://github.com/tqchen/xgboost/issues?q=is%3Aissue+label%3Aquestion)
Features
xgboost-unity
=======
* Sparse feature format:
- Sparse feature format allows easy handling of missing values, and improve computation efficiency.
* Push the limit on single machine:
- Efficient implementation that optimizes memory and computation.
* Speed: XGBoost is very fast
- IN [demo/higgs/speedtest.py](demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
* Layout of gradient boosting algorithm to support user defined objective
* Python interface, works with numpy and scipy.sparse matrix
Supported key components
=======
* Gradient boosting models:
- regression tree (GBRT)
- linear model/lasso
* Objectives to support tasks:
- regression
- classification
* OpenMP implementation
Planned components
=======
* More objective to support tasks:
- ranking
- matrix factorization
- structured prediction
experimental branch: refactor xgboost, cleaner code, more flexibility
Build
======
@ -42,8 +18,17 @@ Build
* If your compiler does not come with OpenMP support, it will fire an warning telling you that the code will compile into single thread mode, and you will get single thread xgboost
- You may get a error: -lgomp is not found, you can remove -fopenmp flag in Makefile to get single thread xgboost, or upgrade your compiler to compile multi-thread version
File extension convention
Project Logical Layout
=======
* .h are interface, utils and data structures, with detailed comment;
* .cpp are implementations that will be compiled, with less comment;
* .hpp are implementations that will be included by .cpp, with less comment
* Dependency order: learner->gbm->tree
* 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
File Naming Convention
=======
* The project is templatized, to make it easy to adjust input data structure.
* .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

200
booster/linear/xgboost_linear.hpp
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@ -1,200 +0,0 @@
#ifndef XGBOOST_LINEAR_HPP
#define XGBOOST_LINEAR_HPP
/*!
* \file xgboost_linear.h
* \brief Implementation of Linear booster, with L1/L2 regularization: Elastic Net
* the update rule is coordinate descent, require column major format
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <vector>
#include <algorithm>
#include "../xgboost.h"
#include "../../utils/xgboost_utils.h"
namespace xgboost{
namespace booster{
/*! \brief linear model, with L1/L2 regularization */
template<typename FMatrix>
class LinearBooster : public InterfaceBooster<FMatrix>{
public:
LinearBooster( void ){ silent = 0;}
virtual ~LinearBooster( void ){}
public:
virtual void SetParam( const char *name, const char *val ){
if( !strcmp( name, "silent") ) silent = atoi( val );
if( model.weight.size() == 0 ) model.param.SetParam( name, val );
param.SetParam( name, val );
}
virtual void LoadModel( utils::IStream &fi ){
model.LoadModel( fi );
}
virtual void SaveModel( utils::IStream &fo ) const{
model.SaveModel( fo );
}
virtual void InitModel( void ){
model.InitModel();
}
public:
virtual void DoBoost( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &fmat,
const std::vector<unsigned> &root_index ){
utils::Assert( grad.size() < UINT_MAX, "number of instance exceed what we can handle" );
this->UpdateWeights( grad, hess, fmat );
}
inline float Predict( const FMatrix &fmat, bst_uint ridx, unsigned root_index ){
float sum = model.bias();
for( typename FMatrix::RowIter it = fmat.GetRow(ridx); it.Next(); ){
sum += model.weight[ it.findex() ] * it.fvalue();
}
return sum;
}
virtual float Predict( const std::vector<float> &feat,
const std::vector<bool> &funknown,
unsigned rid = 0 ){
float sum = model.bias();
for( size_t i = 0; i < feat.size(); i ++ ){
if( funknown[i] ) continue;
sum += model.weight[ i ] * feat[ i ];
}
return sum;
}
protected:
// training parameter
struct ParamTrain{
/*! \brief learning_rate */
float learning_rate;
/*! \brief regularization weight for L2 norm */
float reg_lambda;
/*! \brief regularization weight for L1 norm */
float reg_alpha;
/*! \brief regularization weight for L2 norm in bias */
float reg_lambda_bias;
ParamTrain( void ){
reg_alpha = 0.0f; reg_lambda = 0.0f; reg_lambda_bias = 0.0f;
learning_rate = 1.0f;
}
inline void SetParam( const char *name, const char *val ){
// sync-names
if( !strcmp( "eta", name ) ) learning_rate = (float)atof( val );
if( !strcmp( "lambda", name ) ) reg_lambda = (float)atof( val );
if( !strcmp( "alpha", name ) ) reg_alpha = (float)atof( val );
if( !strcmp( "lambda_bias", name ) ) reg_lambda_bias = (float)atof( val );
// real names
if( !strcmp( "learning_rate", name ) ) learning_rate = (float)atof( val );
if( !strcmp( "reg_lambda", name ) ) reg_lambda = (float)atof( val );
if( !strcmp( "reg_alpha", name ) ) reg_alpha = (float)atof( val );
if( !strcmp( "reg_lambda_bias", name ) ) reg_lambda_bias = (float)atof( val );
}
// given original weight calculate delta
inline double CalcDelta( double sum_grad, double sum_hess, double w ){
if( sum_hess < 1e-5f ) return 0.0f;
double tmp = w - ( sum_grad + reg_lambda*w )/( sum_hess + reg_lambda );
if ( tmp >=0 ){
return std::max(-( sum_grad + reg_lambda*w + reg_alpha)/(sum_hess+reg_lambda),-w);
}else{
return std::min(-( sum_grad + reg_lambda*w - reg_alpha)/(sum_hess+reg_lambda),-w);
}
}
// given original weight calculate delta bias
inline double CalcDeltaBias( double sum_grad, double sum_hess, double w ){
return - (sum_grad + reg_lambda_bias*w) / (sum_hess + reg_lambda_bias );
}
};
// model for linear booster
class Model{
public:
// model parameter
struct Param{
// number of feature dimension
int num_feature;
// reserved field
int reserved[ 32 ];
// constructor
Param( void ){
num_feature = 0;
memset( reserved, 0, sizeof(reserved) );
}
inline void SetParam( const char *name, const char *val ){
if( !strcmp( name, "num_feature" ) ) num_feature = atoi( val );
}
};
public:
Param param;
// weight for each of feature, bias is the last one
std::vector<float> weight;
public:
// initialize the model parameter
inline void InitModel( void ){
// bias is the last weight
weight.resize( param.num_feature + 1 );
std::fill( weight.begin(), weight.end(), 0.0f );
}
// save the model to file
inline void SaveModel( utils::IStream &fo ) const{
fo.Write( &param, sizeof(Param) );
fo.Write( &weight[0], sizeof(float) * weight.size() );
}
// load model from file
inline void LoadModel( utils::IStream &fi ){
utils::Assert( fi.Read( &param, sizeof(Param) ) != 0, "Load LinearBooster" );
weight.resize( param.num_feature + 1 );
utils::Assert( fi.Read( &weight[0], sizeof(float) * weight.size() ) != 0, "Load LinearBooster" );
}
// model bias
inline float &bias( void ){
return weight.back();
}
};
private:
int silent;
protected:
Model model;
ParamTrain param;
protected:
// update weights, should work for any FMatrix
inline void UpdateWeights( std::vector<float> &grad,
const std::vector<float> &hess,
const FMatrix &smat ){
{// optimize bias
double sum_grad = 0.0, sum_hess = 0.0;
for( size_t i = 0; i < grad.size(); i ++ ){
sum_grad += grad[ i ]; sum_hess += hess[ i ];
}
// remove bias effect
double dw = param.learning_rate * param.CalcDeltaBias( sum_grad, sum_hess, model.bias() );
model.bias() += dw;
// update grad value
for( size_t i = 0; i < grad.size(); i ++ ){
grad[ i ] += dw * hess[ i ];
}
}
// optimize weight
const unsigned nfeat= (unsigned)smat.NumCol();
for( unsigned i = 0; i < nfeat; i ++ ){
if( !smat.GetSortedCol( i ).Next() ) continue;
double sum_grad = 0.0, sum_hess = 0.0;
for( typename FMatrix::ColIter it = smat.GetSortedCol(i); it.Next(); ){
const float v = it.fvalue();
sum_grad += grad[ it.rindex() ] * v;
sum_hess += hess[ it.rindex() ] * v * v;
}
float w = model.weight[ i ];
double dw = param.learning_rate * param.CalcDelta( sum_grad, sum_hess, w );
model.weight[ i ] += dw;
// update grad value
for( typename FMatrix::ColIter it = smat.GetSortedCol(i); it.Next(); ){
const float v = it.fvalue();
grad[ it.rindex() ] += hess[ it.rindex() ] * v * dw;
}
}
}
};
};
};
#endif

147
booster/tree/xgboost_base_treemaker.hpp
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@ -1,147 +0,0 @@
#ifndef XGBOOST_BASE_TREEMAKER_HPP
#define XGBOOST_BASE_TREEMAKER_HPP
/*!
* \file xgboost_base_treemaker.hpp
* \brief implementation of base data structure for regression tree maker,
* gives common operations of tree construction steps template
*
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <vector>
#include "xgboost_tree_model.h"
namespace xgboost{
namespace booster{
class BaseTreeMaker{
protected:
BaseTreeMaker( RegTree &tree,
const TreeParamTrain &param )
: tree( tree ), param( param ){}
protected:
// statistics that is helpful to decide a split
struct SplitEntry{
/*! \brief loss change after split this node */
float loss_chg;
/*! \brief split index */
unsigned sindex;
/*! \brief split value */
float split_value;
/*! \brief constructor */
SplitEntry( void ){
loss_chg = 0.0f;
split_value = 0.0f; sindex = 0;
}
// This function gives better priority to lower index when loss_chg equals
// not the best way, but helps to give consistent result during multi-thread execution
inline bool NeedReplace( float loss_chg, unsigned split_index ) const{
if( this->split_index() <= split_index ){
return loss_chg > this->loss_chg;
}else{
return !(this->loss_chg > loss_chg);
}
}
inline bool Update( const SplitEntry &e ){
if( this->NeedReplace( e.loss_chg, e.split_index() ) ){
this->loss_chg = e.loss_chg;
this->sindex = e.sindex;
this->split_value = e.split_value;
return true;
} else{
return false;
}
}
inline bool Update( float loss_chg, unsigned split_index, float split_value, bool default_left ){
if( this->NeedReplace( loss_chg, split_index ) ){
this->loss_chg = loss_chg;
if( default_left ) split_index |= (1U << 31);
this->sindex = split_index;
this->split_value = split_value;
return true;
}else{
return false;
}
}
inline unsigned split_index( void ) const{
return sindex & ( (1U<<31) - 1U );
}
inline bool default_left( void ) const{
return (sindex >> 31) != 0;
}
};
struct NodeEntry{
/*! \brief sum gradient statistics */
double sum_grad;
/*! \brief sum hessian statistics */
double sum_hess;
/*! \brief loss of this node, without split */
float root_gain;
/*! \brief weight calculated related to current data */
float weight;
/*! \brief current best solution */
SplitEntry best;
NodeEntry( void ){
sum_grad = sum_hess = 0.0;
weight = root_gain = 0.0f;
}
};
private:
// try to prune off current leaf, return true if successful
inline void TryPruneLeaf( int nid, int depth ){
if( tree[ nid ].is_root() ) return;
int pid = tree[ nid ].parent();
RegTree::NodeStat &s = tree.stat( pid );
++ s.leaf_child_cnt;
if( s.leaf_child_cnt >= 2 && param.need_prune( s.loss_chg, depth - 1 ) ){
this->stat_num_pruned += 2;
// need to be pruned
tree.ChangeToLeaf( pid, param.learning_rate * s.base_weight );
// tail recursion
this->TryPruneLeaf( pid, depth - 1 );
}
}
protected:
/*! \brief do prunning of a tree */
inline int DoPrune( void ){
this->stat_num_pruned = 0;
// initialize auxiliary statistics
for( int nid = 0; nid < tree.param.num_nodes; ++ nid ){
tree.stat( nid ).leaf_child_cnt = 0;
tree.stat( nid ).loss_chg = snode[ nid ].best.loss_chg;
tree.stat( nid ).sum_hess = static_cast<float>( snode[ nid ].sum_hess );
}
for( int nid = 0; nid < tree.param.num_nodes; ++ nid ){
if( tree[ nid ].is_leaf() ) this->TryPruneLeaf( nid, tree.GetDepth(nid) );
}
return this->stat_num_pruned;
}
protected:
/*! \brief update queue expand add in new leaves */
inline void UpdateQueueExpand( std::vector<int> &qexpand ){
std::vector<int> newnodes;
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[i];
if( !tree[ nid ].is_leaf() ){
newnodes.push_back( tree[nid].cleft() );
newnodes.push_back( tree[nid].cright() );
}
}
// use new nodes for qexpand
qexpand = newnodes;
}
protected:
// local helper tmp data structure
// statistics
int stat_num_pruned;
/*! \brief queue of nodes to be expanded */
std::vector<int> qexpand;
/*! \brief TreeNode Data: statistics for each constructed node, the derived class must maintain this */
std::vector<NodeEntry> snode;
protected:
// original data that supports tree construction
RegTree &tree;
const TreeParamTrain &param;
};
}; // namespace booster
}; // namespace xgboost
#endif // XGBOOST_BASE_TREEMAKER_HPP

335
booster/tree/xgboost_col_treemaker.hpp
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#ifndef XGBOOST_COL_TREEMAKER_HPP
#define XGBOOST_COL_TREEMAKER_HPP
/*!
* \file xgboost_col_treemaker.hpp
* \brief implementation of regression tree maker,
* use a column based approach, with OpenMP
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
// use openmp
#include <vector>
#include "xgboost_tree_model.h"
#include "../../utils/xgboost_omp.h"
#include "../../utils/xgboost_random.h"
#include "../../utils/xgboost_fmap.h"
#include "xgboost_base_treemaker.hpp"
namespace xgboost{
namespace booster{
template<typename FMatrix>
class ColTreeMaker : protected BaseTreeMaker{
public:
ColTreeMaker( RegTree &tree,
const TreeParamTrain &param,
const std::vector<float> &grad,
const std::vector<float> &hess,
const FMatrix &smat,
const std::vector<unsigned> &root_index,
const utils::FeatConstrain &constrain )
: BaseTreeMaker( tree, param ),
grad(grad), hess(hess),
smat(smat), root_index(root_index), constrain(constrain) {
utils::Assert( grad.size() == hess.size(), "booster:invalid input" );
utils::Assert( smat.NumRow() == hess.size(), "booster:invalid input" );
utils::Assert( root_index.size() == 0 || root_index.size() == hess.size(), "booster:invalid input" );
utils::Assert( smat.HaveColAccess(), "ColTreeMaker: need column access matrix" );
}
inline void Make( int& stat_max_depth, int& stat_num_pruned ){
this->InitData();
this->InitNewNode( this->qexpand );
stat_max_depth = 0;
for( int depth = 0; depth < param.max_depth; ++ depth ){
this->FindSplit( depth );
this->UpdateQueueExpand( this->qexpand );
this->InitNewNode( this->qexpand );
// if nothing left to be expand, break
if( qexpand.size() == 0 ) break;
stat_max_depth = depth + 1;
}
// set all the rest expanding nodes to leaf
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[i];
tree[ nid ].set_leaf( snode[nid].weight * param.learning_rate );
}
// start prunning the tree
stat_num_pruned = this->DoPrune();
}
private:
/*! \brief per thread x per node entry to store tmp data */
struct ThreadEntry{
/*! \brief sum gradient statistics */
double sum_grad;
/*! \brief sum hessian statistics */
double sum_hess;
/*! \brief last feature value scanned */
float last_fvalue;
/*! \brief current best solution */
SplitEntry best;
/*! \brief constructor */
ThreadEntry( void ){
this->ClearStats();
}
/*! \brief clear statistics */
inline void ClearStats( void ){
sum_grad = sum_hess = 0.0;
}
};
private:
// make leaf nodes for all qexpand, update node statistics, mark leaf value
inline void InitNewNode( const std::vector<int> &qexpand ){
{// setup statistics space for each tree node
for( size_t i = 0; i < stemp.size(); ++ i ){
stemp[i].resize( tree.param.num_nodes, ThreadEntry() );
}
snode.resize( tree.param.num_nodes, NodeEntry() );
}
const unsigned ndata = static_cast<unsigned>( position.size() );
#pragma omp parallel for schedule( static )
for( unsigned i = 0; i < ndata; ++ i ){
const int tid = omp_get_thread_num();
if( position[i] < 0 ) continue;
stemp[tid][ position[i] ].sum_grad += grad[i];
stemp[tid][ position[i] ].sum_hess += hess[i];
}
for( size_t j = 0; j < qexpand.size(); ++ j ){
const int nid = qexpand[ j ];
double sum_grad = 0.0, sum_hess = 0.0;
for( size_t tid = 0; tid < stemp.size(); tid ++ ){
sum_grad += stemp[tid][nid].sum_grad;
sum_hess += stemp[tid][nid].sum_hess;
}
// update node statistics
snode[nid].sum_grad = sum_grad;
snode[nid].sum_hess = sum_hess;
snode[nid].root_gain = param.CalcRootGain( sum_grad, sum_hess );
if( !tree[nid].is_root() ){
snode[nid].weight = param.CalcWeight( sum_grad, sum_hess, tree.stat( tree[nid].parent() ).base_weight );
tree.stat(nid).base_weight = snode[nid].weight;
}else{
snode[nid].weight = param.CalcWeight( sum_grad, sum_hess, 0.0f );
tree.stat(nid).base_weight = snode[nid].weight;
}
}
}
private:
// enumerate the split values of specific feature
template<typename Iter>
inline void EnumerateSplit( Iter it, const unsigned fid, std::vector<ThreadEntry> &temp, bool is_forward_search ){
// clear all the temp statistics
for( size_t j = 0; j < qexpand.size(); ++ j ){
temp[ qexpand[j] ].ClearStats();
}
while( it.Next() ){
const bst_uint ridx = it.rindex();
const int nid = position[ ridx ];
if( nid < 0 ) continue;
const float fvalue = it.fvalue();
ThreadEntry &e = temp[ nid ];
// test if first hit, this is fine, because we set 0 during init
if( e.sum_hess == 0.0 ){
e.sum_grad = grad[ ridx ];
e.sum_hess = hess[ ridx ];
e.last_fvalue = fvalue;
}else{
// try to find a split
if( fabsf(fvalue - e.last_fvalue) > rt_2eps && e.sum_hess >= param.min_child_weight ){
const double csum_hess = snode[ nid ].sum_hess - e.sum_hess;
if( csum_hess >= param.min_child_weight ){
const double csum_grad = snode[nid].sum_grad - e.sum_grad;
const double loss_chg =
+ param.CalcGain( e.sum_grad, e.sum_hess, snode[nid].weight )
+ param.CalcGain( csum_grad , csum_hess , snode[nid].weight )
- snode[nid].root_gain;
e.best.Update( loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f, !is_forward_search );
}
}
// update the statistics
e.sum_grad += grad[ ridx ];
e.sum_hess += hess[ ridx ];
e.last_fvalue = fvalue;
}
}
// finish updating all statistics, check if it is possible to include all sum statistics
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[ i ];
ThreadEntry &e = temp[ nid ];
const double csum_hess = snode[nid].sum_hess - e.sum_hess;
if( e.sum_hess >= param.min_child_weight && csum_hess >= param.min_child_weight ){
const double csum_grad = snode[nid].sum_grad - e.sum_grad;
const double loss_chg =
+ param.CalcGain( e.sum_grad, e.sum_hess, snode[nid].weight )
+ param.CalcGain( csum_grad, csum_hess, snode[nid].weight )
- snode[nid].root_gain;
const float delta = is_forward_search ? rt_eps:-rt_eps;
e.best.Update( loss_chg, fid, e.last_fvalue + delta, !is_forward_search );
}
}
}
// find splits at current level
inline void FindSplit( int depth ){
const unsigned nsize = static_cast<unsigned>( feat_index.size() );
#pragma omp parallel for schedule( dynamic, 1 )
for( unsigned i = 0; i < nsize; ++ i ){
const unsigned fid = feat_index[i];
const int tid = omp_get_thread_num();
if( param.need_forward_search() ){
this->EnumerateSplit( smat.GetSortedCol(fid), fid, stemp[tid], true );
}
if( param.need_backward_search() ){
this->EnumerateSplit( smat.GetReverseSortedCol(fid), fid, stemp[tid], false );
}
}
// after this each thread's stemp will get the best candidates, aggregate results
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[ i ];
NodeEntry &e = snode[ nid ];
for( int tid = 0; tid < this->nthread; ++ tid ){
e.best.Update( stemp[ tid ][ nid ].best );
}
// now we know the solution in snode[ nid ], set split
if( e.best.loss_chg > rt_eps ){
tree.AddChilds( nid );
tree[ nid ].set_split( e.best.split_index(), e.best.split_value, e.best.default_left() );
} else{
tree[ nid ].set_leaf( e.weight * param.learning_rate );
}
}
{// reset position
// step 1, set default direct nodes to default, and leaf nodes to -1,
const unsigned ndata = static_cast<unsigned>( position.size() );
#pragma omp parallel for schedule( static )
for( unsigned i = 0; i < ndata; ++ i ){
const int nid = position[i];
if( nid >= 0 ){
if( tree[ nid ].is_leaf() ){
position[i] = -1;
}else{
// push to default branch, correct latter
position[i] = tree[nid].default_left() ? tree[nid].cleft(): tree[nid].cright();
}
}
}
// step 2, classify the non-default data into right places
std::vector<unsigned> fsplits;
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[i];
if( !tree[nid].is_leaf() ) fsplits.push_back( tree[nid].split_index() );
}
std::sort( fsplits.begin(), fsplits.end() );
fsplits.resize( std::unique( fsplits.begin(), fsplits.end() ) - fsplits.begin() );
const unsigned nfeats = static_cast<unsigned>( fsplits.size() );
#pragma omp parallel for schedule( dynamic, 1 )
for( unsigned i = 0; i < nfeats; ++ i ){
const unsigned fid = fsplits[i];
for( typename FMatrix::ColIter it = smat.GetSortedCol( fid ); it.Next(); ){
const bst_uint ridx = it.rindex();
int nid = position[ ridx ];
if( nid == -1 ) continue;
// go back to parent, correct those who are not default
nid = tree[ nid ].parent();
if( tree[ nid ].split_index() == fid ){
if( it.fvalue() < tree[nid].split_cond() ){
position[ ridx ] = tree[ nid ].cleft();
}else{
position[ ridx ] = tree[ nid ].cright();
}
}
}
}
}
}
private:
// initialize temp data structure
inline void InitData( void ){
{
position.resize( grad.size() );
if( root_index.size() == 0 ){
std::fill( position.begin(), position.end(), 0 );
}else{
for( size_t i = 0; i < root_index.size(); ++ i ){
position[i] = root_index[i];
utils::Assert( root_index[i] < (unsigned)tree.param.num_roots, "root index exceed setting" );
}
}
// mark delete for the deleted datas
for( size_t i = 0; i < grad.size(); ++ i ){
if( hess[i] < 0.0f ) position[i] = -1;
}
if( param.subsample < 1.0f - 1e-6f ){
for( size_t i = 0; i < grad.size(); ++ i ){
if( hess[i] < 0.0f ) continue;
if( random::SampleBinary( param.subsample) == 0 ){
position[ i ] = -1;
}
}
}
}
{// initialize feature index
int ncol = static_cast<int>( smat.NumCol() );
for( int i = 0; i < ncol; i ++ ){
if( smat.GetSortedCol(i).Next() && constrain.NotBanned(i) ){
feat_index.push_back( i );
}
}
random::Shuffle( feat_index );
}
{// setup temp space for each thread
if( param.nthread != 0 ){
omp_set_num_threads( param.nthread );
}
#pragma omp parallel
{
this->nthread = omp_get_num_threads();
}
// reserve a small space
stemp.resize( this->nthread, std::vector<ThreadEntry>() );
for( size_t i = 0; i < stemp.size(); ++ i ){
stemp[i].reserve( 256 );
}
snode.reserve( 256 );
}
{// expand query
qexpand.reserve( 256 ); qexpand.clear();
for( int i = 0; i < tree.param.num_roots; ++ i ){
qexpand.push_back( i );
}
}
}
private:
// number of omp thread used during training
int nthread;
// Per feature: shuffle index of each feature index
std::vector<int> feat_index;
// Instance Data: current node position in the tree of each instance
std::vector<int> position;
// PerThread x PerTreeNode: statistics for per thread construction
std::vector< std::vector<ThreadEntry> > stemp;
private:
const std::vector<float> &grad;
const std::vector<float> &hess;
const FMatrix &smat;
const std::vector<unsigned> &root_index;
const utils::FeatConstrain &constrain;
};
};
};
#endif

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booster/tree/xgboost_row_treemaker.hpp
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#ifndef XGBOOST_ROW_TREEMAKER_HPP
#define XGBOOST_ROW_TREEMAKER_HPP
/*!
* \file xgboost_row_treemaker.hpp
* \brief implementation of regression tree maker,
* use a row based approach
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
// use openmp
#include <vector>
#include "xgboost_tree_model.h"
#include "../../utils/xgboost_omp.h"
#include "../../utils/xgboost_random.h"
#include "../../utils/xgboost_fmap.h"
#include "xgboost_base_treemaker.hpp"
namespace xgboost{
namespace booster{
template<typename FMatrix>
class RowTreeMaker : protected BaseTreeMaker{
public:
RowTreeMaker( RegTree &tree,
const TreeParamTrain &param,
const std::vector<float> &grad,
const std::vector<float> &hess,
const FMatrix &smat,
const std::vector<unsigned> &root_index,
const utils::FeatConstrain &constrain )
: BaseTreeMaker( tree, param ),
grad(grad), hess(hess),
smat(smat), root_index(root_index), constrain(constrain) {
utils::Assert( grad.size() == hess.size(), "booster:invalid input" );
utils::Assert( smat.NumRow() == hess.size(), "booster:invalid input" );
utils::Assert( root_index.size() == 0 || root_index.size() == hess.size(), "booster:invalid input" );
{// setup temp space for each thread
if( param.nthread != 0 ){
omp_set_num_threads( param.nthread );
}
#pragma omp parallel
{
this->nthread = omp_get_num_threads();
}
tmp_rptr.resize( this->nthread, std::vector<size_t>() );
snode.reserve( 256 );
}
}
inline void Make( int& stat_max_depth, int& stat_num_pruned ){
this->InitData();
this->InitNewNode( this->qexpand );
stat_max_depth = 0;
for( int depth = 0; depth < param.max_depth; ++ depth ){
this->FindSplit( this->qexpand, depth );
this->UpdateQueueExpand( this->qexpand );
this->InitNewNode( this->qexpand );
// if nothing left to be expand, break
if( qexpand.size() == 0 ) break;
stat_max_depth = depth + 1;
}
// set all the rest expanding nodes to leaf
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[i];
tree[ nid ].set_leaf( snode[nid].weight * param.learning_rate );
}
// start prunning the tree
stat_num_pruned = this->DoPrune();
}
// expand a specific node
inline bool Expand( const std::vector<bst_uint> &valid_index, int nid ){
if( valid_index.size() == 0 ) return false;
this->InitDataExpand( valid_index, nid );
this->InitNewNode( this->qexpand );
this->FindSplit( nid, tmp_rptr[0] );
// update node statistics
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[i];
tree.stat( nid ).loss_chg = snode[ nid ].best.loss_chg;
tree.stat( nid ).sum_hess = static_cast<float>( snode[ nid ].sum_hess );
}
// change the leaf
this->UpdateQueueExpand( this->qexpand );
this->InitNewNode( this->qexpand );
// set all the rest expanding nodes to leaf
for( size_t i = 0; i < qexpand.size(); ++ i ){
const int nid = qexpand[i];
tree[ nid ].set_leaf( snode[nid].weight * param.learning_rate );
tree.stat( nid ).loss_chg = 0.0f;
tree.stat( nid ).sum_hess = static_cast<float>( snode[ nid ].sum_hess );
tree.param.max_depth = std::max( tree.param.max_depth, tree.GetDepth( nid ) );
}
if( qexpand.size() != 0 ) {
return true;
}else{
return false;
}
}
// collapse specific node
inline void Collapse( const std::vector<bst_uint> &valid_index, int nid ){
if( valid_index.size() == 0 ) return;
this->InitDataExpand( valid_index, nid );
this->InitNewNode( this->qexpand );
tree.stat( nid ).loss_chg = 0.0f;
tree.stat( nid ).sum_hess = static_cast<float>( snode[ nid ].sum_hess );
tree.CollapseToLeaf( nid, snode[nid].weight * param.learning_rate );
}
private:
// make leaf nodes for all qexpand, update node statistics, mark leaf value
inline void InitNewNode( const std::vector<int> &qexpand ){
snode.resize( tree.param.num_nodes, NodeEntry() );
for( size_t j = 0; j < qexpand.size(); ++j ){
const int nid = qexpand[ j ];
double sum_grad = 0.0, sum_hess = 0.0;
for( bst_uint i = node_bound[nid].first; i < node_bound[nid].second; ++i ){
const bst_uint ridx = row_index_set[i];
sum_grad += grad[ridx]; sum_hess += hess[ridx];
}
// update node statistics
snode[nid].sum_grad = sum_grad;
snode[nid].sum_hess = sum_hess;
snode[nid].root_gain = param.CalcRootGain( sum_grad, sum_hess );
if( !tree[nid].is_root() ){
snode[nid].weight = param.CalcWeight( sum_grad, sum_hess, tree.stat( tree[nid].parent() ).base_weight );
tree.stat(nid).base_weight = snode[nid].weight;
}else{
snode[nid].weight = param.CalcWeight( sum_grad, sum_hess, 0.0f );
tree.stat(nid).base_weight = snode[nid].weight;
}
}
}
private:
// enumerate the split values of specific feature
template<typename Iter>
inline void EnumerateSplit( Iter it, SplitEntry &best, const int nid, const unsigned fid, bool is_forward_search ){
float last_fvalue = 0.0f;
double sum_hess = 0.0, sum_grad = 0.0;
const NodeEntry enode = snode[ nid ];
while( it.Next() ){
const bst_uint ridx = it.rindex();
const float fvalue = it.fvalue();
if( sum_hess == 0.0 ){
sum_grad = grad[ ridx ];
sum_hess = hess[ ridx ];
last_fvalue = fvalue;
}else{
// try to find a split
if( fabsf(fvalue - last_fvalue) > rt_2eps && sum_hess >= param.min_child_weight ){
const double csum_hess = enode.sum_hess - sum_hess;
if( csum_hess >= param.min_child_weight ){
const double csum_grad = enode.sum_grad - sum_grad;
const double loss_chg =
+ param.CalcGain( sum_grad, sum_hess, enode.weight )
+ param.CalcGain( csum_grad, csum_hess, enode.weight )
- enode.root_gain;
best.Update( loss_chg, fid, (fvalue + last_fvalue) * 0.5f, !is_forward_search );
}else{
// the rest part doesn't meet split condition anyway, return
return;
}
}
// update the statistics
sum_grad += grad[ ridx ];
sum_hess += hess[ ridx ];
last_fvalue = fvalue;
}
}
const double csum_hess = enode.sum_hess - sum_hess;
if( sum_hess >= param.min_child_weight && csum_hess >= param.min_child_weight ){
const double csum_grad = enode.sum_grad - sum_grad;
const double loss_chg =
+ param.CalcGain( sum_grad, sum_hess, enode.weight )
+ param.CalcGain( csum_grad, csum_hess, enode.weight )
- snode[nid].root_gain;
const float delta = is_forward_search ? rt_eps:-rt_eps;
best.Update( loss_chg, fid, last_fvalue + delta, !is_forward_search );
}
}
private:
inline void FindSplit( const std::vector<int> &qexpand, int depth ){
int nexpand = (int)qexpand.size();
if( depth < 3 ){
for( int i = 0; i < nexpand; ++ i ){
this->FindSplit( qexpand[i], tmp_rptr[0] );
}
}else{
// if get to enough depth, parallelize over node
#pragma omp parallel for schedule(dynamic,1)
for( int i = 0; i < nexpand; ++ i ){
const int tid = omp_get_thread_num();
utils::Assert( tid < (int)tmp_rptr.size(), "BUG: FindSplit, tid exceed tmp_rptr size" );
this->FindSplit( qexpand[i], tmp_rptr[tid] );
}
}
}
private:
inline void MakeSplit( int nid, unsigned gid ){
node_bound.resize( tree.param.num_nodes );
// re-organize the row_index_set after split on nid
const unsigned split_index = tree[nid].split_index();
const float split_value = tree[nid].split_cond();
std::vector<bst_uint> right;
bst_uint top = node_bound[nid].first;
for( bst_uint i = node_bound[ nid ].first; i < node_bound[ nid ].second; ++i ){
const bst_uint ridx = row_index_set[i];
bool goleft = tree[ nid ].default_left();
for( typename FMatrix::RowIter it = smat.GetRow(ridx,gid); it.Next(); ){
if( it.findex() == split_index ){
if( it.fvalue() < split_value ){
goleft = true; break;
}else{
goleft = false; break;
}
}
}
if( goleft ) {
row_index_set[ top ++ ] = ridx;
}else{
right.push_back( ridx );
}
}
node_bound[ tree[nid].cleft() ] = std::make_pair( node_bound[nid].first, top );
node_bound[ tree[nid].cright() ] = std::make_pair( top, node_bound[nid].second );
utils::Assert( node_bound[nid].second - top == (bst_uint)right.size(), "BUG:MakeSplit" );
for( size_t i = 0; i < right.size(); ++ i ){
row_index_set[ top ++ ] = right[ i ];
}
}
// find splits at current level
inline void FindSplit( int nid, std::vector<size_t> &tmp_rptr ){
if( tmp_rptr.size() == 0 ){
tmp_rptr.resize( tree.param.num_feature + 1, 0 );
}
const bst_uint begin = node_bound[ nid ].first;
const bst_uint end = node_bound[ nid ].second;
const unsigned ncgroup = smat.NumColGroup();
unsigned best_group = 0;
for( unsigned gid = 0; gid < ncgroup; ++gid ){
// records the columns
std::vector<FMatrixS::REntry> centry;
// records the active features
std::vector<size_t> aclist;
utils::SparseCSRMBuilder<FMatrixS::REntry,true> builder( tmp_rptr, centry, aclist );
builder.InitBudget( tree.param.num_feature );
for( bst_uint i = begin; i < end; ++i ){
const bst_uint ridx = row_index_set[i];
for( typename FMatrix::RowIter it = smat.GetRow(ridx,gid); it.Next(); ){
const bst_uint findex = it.findex();
if( constrain.NotBanned( findex ) ) builder.AddBudget( findex );
}
}
builder.InitStorage();
for( bst_uint i = begin; i < end; ++i ){
const bst_uint ridx = row_index_set[i];
for( typename FMatrix::RowIter it = smat.GetRow(ridx,gid); it.Next(); ){
const bst_uint findex = it.findex();
if( constrain.NotBanned( findex ) ) {
builder.PushElem( findex, FMatrixS::REntry( ridx, it.fvalue() ) );
}
}
}
// --- end of building column major matrix ---
// after this point, tmp_rptr and entry is ready to use
int naclist = (int)aclist.size();
// best entry for each thread
SplitEntry nbest, tbest;
#pragma omp parallel private(tbest)
{
#pragma omp for schedule(dynamic,1)
for( int j = 0; j < naclist; ++j ){
bst_uint findex = static_cast<bst_uint>( aclist[j] );
// local sort can be faster when the features are sparse
std::sort( centry.begin() + tmp_rptr[findex], centry.begin() + tmp_rptr[findex+1], FMatrixS::REntry::cmp_fvalue );
if( param.need_forward_search() ){
this->EnumerateSplit( FMatrixS::ColIter( &centry[tmp_rptr[findex]]-1, &centry[tmp_rptr[findex+1]] - 1 ),
tbest, nid, findex, true );
}
if( param.need_backward_search() ){
this->EnumerateSplit( FMatrixS::ColBackIter( &centry[tmp_rptr[findex+1]], &centry[tmp_rptr[findex]] ),
tbest, nid, findex, false );
}
}
#pragma omp critical
{
nbest.Update( tbest );
}
}
// if current solution gives the best
if( snode[nid].best.Update( nbest ) ){
best_group = gid;
}
// cleanup tmp_rptr for next usage
builder.Cleanup();
}
// at this point, we already know the best split
if( snode[nid].best.loss_chg > rt_eps ){
const SplitEntry &e = snode[nid].best;
tree.AddChilds( nid );
tree[ nid ].set_split( e.split_index(), e.split_value, e.default_left() );
this->MakeSplit( nid, best_group );
}else{
tree[ nid ].set_leaf( snode[nid].weight * param.learning_rate );
}
}
private:
// initialize temp data structure
inline void InitData( void ){
std::vector<bst_uint> valid_index;
for( size_t i = 0; i < grad.size(); ++i ){
if( hess[ i ] < 0.0f ) continue;
if( param.subsample > 1.0f-1e-6f || random::SampleBinary( param.subsample ) != 0 ){
valid_index.push_back( static_cast<bst_uint>(i) );
}
}
node_bound.resize( tree.param.num_roots );
if( root_index.size() == 0 ){
row_index_set = valid_index;
// set bound of root node
node_bound[0] = std::make_pair( 0, (bst_uint)row_index_set.size() );
}else{
std::vector<size_t> rptr;
utils::SparseCSRMBuilder<bst_uint> builder( rptr, row_index_set );
builder.InitBudget( tree.param.num_roots );
for( size_t i = 0; i < valid_index.size(); ++i ){
const bst_uint rid = valid_index[ i ];
utils::Assert( root_index[ rid ] < (unsigned)tree.param.num_roots, "root id exceed number of roots" );
builder.AddBudget( root_index[ rid ] );
}
builder.InitStorage();
for( size_t i = 0; i < valid_index.size(); ++i ){
const bst_uint rid = valid_index[ i ];
builder.PushElem( root_index[ rid ], rid );
}
for( size_t i = 1; i < rptr.size(); ++ i ){
node_bound[i-1] = std::make_pair( rptr[ i - 1 ], rptr[ i ] );
}
}
{// expand query
qexpand.reserve( 256 ); qexpand.clear();
for( int i = 0; i < tree.param.num_roots; ++ i ){
qexpand.push_back( i );
}
}
}
// initialize temp data structure
inline void InitDataExpand( const std::vector<bst_uint> &valid_index, int nid ){
row_index_set = valid_index;
node_bound.resize( tree.param.num_nodes );
node_bound[ nid ] = std::make_pair( 0, (bst_uint)row_index_set.size() );
qexpand.clear(); qexpand.push_back( nid );
}
private:
// number of omp thread used during training
int nthread;
// tmp row pointer, per thread, used for tmp data construction
std::vector< std::vector<size_t> > tmp_rptr;
// Instance row indexes corresponding to each node
std::vector<bst_uint> row_index_set;
// lower and upper bound of each nodes' row_index
std::vector< std::pair<bst_uint, bst_uint> > node_bound;
private:
const std::vector<float> &grad;
const std::vector<float> &hess;
const FMatrix &smat;
const std::vector<unsigned> &root_index;
const utils::FeatConstrain &constrain;
};
};
};
#endif

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booster/tree/xgboost_svdf_tree.hpp
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#ifndef XGBOOST_APEX_TREE_HPP
#define XGBOOST_APEX_TREE_HPP
/*!
* \file xgboost_svdf_tree.hpp
* \brief implementation of regression tree constructor, with layerwise support
* this file is adapted from GBRT implementation in SVDFeature project
* \author Tianqi Chen: tqchen@apex.sjtu.edu.cn, tianqi.tchen@gmail.com
*/
#include <algorithm>
#include "xgboost_tree_model.h"
#include "../../utils/xgboost_random.h"
#include "../../utils/xgboost_matrix_csr.h"
namespace xgboost{
namespace booster{
inline void assert_sorted( unsigned *idset, int len ){
if( !rt_debug || !check_bug ) return;
for( int i = 1; i < len; i ++ ){
utils::Assert( idset[i-1] < idset[i], "idset not sorted" );
}
}
};
namespace booster{
// selecter of rtree to find the suitable candidate
class RTSelecter{
public:
struct Entry{
float loss_chg;
size_t start;
int len;
unsigned sindex;
float split_value;
Entry(){}
Entry( float loss_chg, size_t start, int len, unsigned split_index, float split_value, bool default_left ){
this->loss_chg = loss_chg;
this->start = start;
this->len = len;
if( default_left ) split_index |= (1U << 31);
this->sindex = split_index;
this->split_value = split_value;
}
inline unsigned split_index( void ) const{
return sindex & ( (1U<<31) - 1U );
}
inline bool default_left( void ) const{
return (sindex >> 31) != 0;
}
};
private:
Entry best_entry;
public:
RTSelecter( void ){
memset( &best_entry, 0, sizeof(best_entry) );
best_entry.loss_chg = 0.0f;
}
inline void push_back( const Entry &e ){
if( e.loss_chg > best_entry.loss_chg ) best_entry = e;
}
inline const Entry & select( void ){
return best_entry;
}
};
// updater of rtree, allows the parameters to be stored inside, key solver
template<typename FMatrix>
class RTreeUpdater{
protected:
// training task, element of single task
struct Task{
// node id in tree
int nid;
// idset pointer, instance id in [idset,idset+len)
unsigned *idset;
// length of idset
unsigned len;
// base_weight of parent
float parent_base_weight;
Task(){}
Task( int nid, unsigned *idset, unsigned len, float pweight = 0.0f ){
this->nid = nid;
this->idset = idset;
this->len = len;
this->parent_base_weight = pweight;
}
};
// sparse column entry
struct SCEntry{
// feature value
float fvalue;
// row index in grad
unsigned rindex;
SCEntry(){}
SCEntry( float fvalue, unsigned rindex ){
this->fvalue = fvalue; this->rindex = rindex;
}
inline bool operator<( const SCEntry &p ) const{
return fvalue < p.fvalue;
}
};
private:
// training parameter
const TreeParamTrain &param;
// parameters, reference
RegTree &tree;
std::vector<float> &grad;
std::vector<float> &hess;
const FMatrix &smat;
const std::vector<unsigned> &group_id;
private:
// maximum depth up to now
int max_depth;
// number of nodes being pruned
int num_pruned;
// stack to store current task
std::vector<Task> task_stack;
// temporal space for index set
std::vector<unsigned> idset;
private:
// task management: NOTE DFS here
inline void add_task( Task tsk ){
task_stack.push_back( tsk );
}
inline bool next_task( Task &tsk ){
if( task_stack.size() == 0 ) return false;
tsk = task_stack.back();
task_stack.pop_back();
return true;
}
private:
// try to prune off current leaf, return true if successful
inline void try_prune_leaf( int nid, int depth ){
if( tree[ nid ].is_root() ) return;
int pid = tree[ nid ].parent();
RegTree::NodeStat &s = tree.stat( pid );
s.leaf_child_cnt ++;
if( s.leaf_child_cnt >= 2 && param.need_prune( s.loss_chg, depth - 1 ) ){
// need to be pruned
tree.ChangeToLeaf( pid, param.learning_rate * s.base_weight );
// add statistics to number of nodes pruned
num_pruned += 2;
// tail recursion
this->try_prune_leaf( pid, depth - 1 );
}
}
// make leaf for current node :)
inline void make_leaf( Task tsk, double sum_grad, double sum_hess, bool compute ){
for( unsigned i = 0; i < tsk.len; i ++ ){
const unsigned ridx = tsk.idset[i];
if( compute ){
sum_grad += grad[ ridx ];
sum_hess += hess[ ridx ];
}
}
tree.stat( tsk.nid ).sum_hess = static_cast<float>( sum_hess );
tree[ tsk.nid ].set_leaf( param.learning_rate * param.CalcWeight( sum_grad, sum_hess, tsk.parent_base_weight ) );
this->try_prune_leaf( tsk.nid, tree.GetDepth( tsk.nid ) );
}
private:
// make split for current task, re-arrange positions in idset
inline void make_split( Task tsk, const SCEntry *entry, int num, float loss_chg, double sum_hess, double base_weight ){
// before split, first prepare statistics
RegTree::NodeStat &s = tree.stat( tsk.nid );
s.loss_chg = loss_chg;
s.leaf_child_cnt = 0;
s.sum_hess = static_cast<float>( sum_hess );
s.base_weight = static_cast<float>( base_weight );
// add childs to current node
tree.AddChilds( tsk.nid );
// assert that idset is sorted
assert_sorted( tsk.idset, tsk.len );
// use merge sort style to get the solution
std::vector<unsigned> qset;
for( int i = 0; i < num; i ++ ){
qset.push_back( entry[i].rindex );
}
std::sort( qset.begin(), qset.end() );
// do merge sort style, make the other set, remove elements in qset
for( unsigned i = 0, top = 0; i < tsk.len; i ++ ){
if( top < qset.size() ){
if( tsk.idset[ i ] != qset[ top ] ){
tsk.idset[ i - top ] = tsk.idset[ i ];
}else{
top ++;
}
}else{
tsk.idset[ i - qset.size() ] = tsk.idset[ i ];
}
}
// get two parts
RegTree::Node &n = tree[ tsk.nid ];
Task def_part( n.default_left() ? n.cleft() : n.cright(), tsk.idset, tsk.len - qset.size(), s.base_weight );
Task spl_part( n.default_left() ? n.cright(): n.cleft() , tsk.idset + def_part.len, qset.size(), s.base_weight );
// fill back split part
for( unsigned i = 0; i < spl_part.len; i ++ ){
spl_part.idset[ i ] = qset[ i ];
}
// add tasks to the queue
this->add_task( def_part );
this->add_task( spl_part );
}
// enumerate split point of the tree
inline void enumerate_split( RTSelecter &sglobal, int tlen,
double rsum_grad, double rsum_hess, double root_gain,
const SCEntry *entry, size_t start, size_t end,
int findex, float parent_base_weight ){
// local selecter
RTSelecter slocal;
if( param.need_forward_search() ){
// forward process, default right
double csum_grad = 0.0, csum_hess = 0.0;
for( size_t j = start; j < end; j ++ ){
const unsigned ridx = entry[ j ].rindex;
csum_grad += grad[ ridx ];
csum_hess += hess[ ridx ];
// check for split
if( j == end - 1 || entry[j].fvalue + rt_2eps < entry[ j + 1 ].fvalue ){
if( csum_hess < param.min_child_weight ) continue;
const double dsum_hess = rsum_hess - csum_hess;
if( dsum_hess < param.min_child_weight ) break;
// change of loss
double loss_chg =
param.CalcGain( csum_grad, csum_hess, parent_base_weight ) +
param.CalcGain( rsum_grad - csum_grad, dsum_hess, parent_base_weight ) - root_gain;
const int clen = static_cast<int>( j + 1 - start );
// add candidate to selecter
slocal.push_back( RTSelecter::Entry( loss_chg, start, clen, findex,
j == end - 1 ? entry[j].fvalue + rt_eps : 0.5 * (entry[j].fvalue+entry[j+1].fvalue),
false ) );
}
}
}
if( param.need_backward_search() ){
// backward process, default left
double csum_grad = 0.0, csum_hess = 0.0;
for( size_t j = end; j > start; j -- ){
const unsigned ridx = entry[ j - 1 ].rindex;
csum_grad += grad[ ridx ];
csum_hess += hess[ ridx ];
// check for split
if( j == start + 1 || entry[ j - 2 ].fvalue + rt_2eps < entry[ j - 1 ].fvalue ){
if( csum_hess < param.min_child_weight ) continue;
const double dsum_hess = rsum_hess - csum_hess;
if( dsum_hess < param.min_child_weight ) break;
double loss_chg = param.CalcGain( csum_grad, csum_hess, parent_base_weight ) +
param.CalcGain( rsum_grad - csum_grad, dsum_hess, parent_base_weight ) - root_gain;
const int clen = static_cast<int>( end - j + 1 );
// add candidate to selecter
slocal.push_back( RTSelecter::Entry( loss_chg, j - 1, clen, findex,
j == start + 1 ? entry[j-1].fvalue - rt_eps : 0.5 * (entry[j-2].fvalue + entry[j-1].fvalue),
true ) );
}
}
}
sglobal.push_back( slocal.select() );
}
private:
// temporal storage for expand column major
std::vector<size_t> tmp_rptr;
// find split for current task, another implementation of expand in column major manner
// should be more memory frugal, avoid global sorting across feature
inline void expand( Task tsk ){
// assert that idset is sorted
// if reach maximum depth, make leaf from current node
int depth = tree.GetDepth( tsk.nid );
// update statistiss
if( depth > max_depth ) max_depth = depth;
// if bigger than max depth
if( depth >= param.max_depth ){
this->make_leaf( tsk, 0.0, 0.0, true ); return;
}
// convert to column major CSR format
const int nrows = tree.param.num_feature;
if( tmp_rptr.size() == 0 ){
// initialize tmp storage in first usage
tmp_rptr.resize( nrows + 1 );
std::fill( tmp_rptr.begin(), tmp_rptr.end(), 0 );
}
// records the columns
std::vector<SCEntry> entry;
// records the active features
std::vector<size_t> aclist;
utils::SparseCSRMBuilder<SCEntry,true> builder( tmp_rptr, entry, aclist );
builder.InitBudget( nrows );
// statistics of root
double rsum_grad = 0.0, rsum_hess = 0.0;
for( unsigned i = 0; i < tsk.len; i ++ ){
const unsigned ridx = tsk.idset[i];
rsum_grad += grad[ ridx ];
rsum_hess += hess[ ridx ];
for( typename FMatrix::RowIter it = smat.GetRow(ridx); it.Next(); ){
builder.AddBudget( it.findex() );
}
}
// if minimum split weight is not meet
if( param.cannot_split( rsum_hess, depth ) ){
this->make_leaf( tsk, rsum_grad, rsum_hess, false ); builder.Cleanup(); return;
}
builder.InitStorage();
for( unsigned i = 0; i < tsk.len; i ++ ){
const unsigned ridx = tsk.idset[i];
for( typename FMatrix::RowIter it = smat.GetRow(ridx); it.Next(); ){
builder.PushElem( it.findex(), SCEntry( it.fvalue(), ridx ) );
}
}
// --- end of building column major matrix ---
// after this point, tmp_rptr and entry is ready to use
// global selecter
RTSelecter sglobal;
// gain root
const double root_gain = param.CalcRootGain( rsum_grad, rsum_hess );
// KEY: layerwise, weight of current node if it is leaf
const double base_weight = param.CalcWeight( rsum_grad, rsum_hess, tsk.parent_base_weight );
// enumerate feature index
for( size_t i = 0; i < aclist.size(); i ++ ){
int findex = static_cast<int>( aclist[i] );
size_t start = tmp_rptr[ findex ];
size_t end = tmp_rptr[ findex + 1 ];
utils::Assert( start < end, "bug" );
// local sort can be faster when the features are sparse
std::sort( entry.begin() + start, entry.begin() + end );
// local selecter
this->enumerate_split( sglobal, tsk.len,
rsum_grad, rsum_hess, root_gain,
&entry[0], start, end, findex, base_weight );
}
// Cleanup tmp_rptr for next use
builder.Cleanup();
// get the best solution
const RTSelecter::Entry &e = sglobal.select();
// allowed to split
if( e.loss_chg > rt_eps ){
// add splits
tree[ tsk.nid ].set_split( e.split_index(), e.split_value, e.default_left() );
// re-arrange idset, push tasks
this->make_split( tsk, &entry[ e.start ], e.len, e.loss_chg, rsum_hess, base_weight );
}else{
// make leaf if we didn't meet requirement
this->make_leaf( tsk, rsum_grad, rsum_hess, false );
}
}
private:
// initialize the tasks
inline void init_tasks( size_t ngrads ){
// add group partition if necessary
if( group_id.size() == 0 ){
if( param.subsample > 1.0f - 1e-6f ){
idset.resize( 0 );
for( size_t i = 0; i < ngrads; i ++ ){
if( hess[i] < 0.0f ) continue;
idset.push_back( (unsigned)i );
}
}else{
idset.resize( 0 );
for( size_t i = 0; i < ngrads; i ++ ){
if( random::SampleBinary( param.subsample ) != 0 ){
idset.push_back( (unsigned)i );
}
}
}
this->add_task( Task( 0, &idset[0], idset.size() ) ); return;
}
utils::Assert( group_id.size() == ngrads, "number of groups must be exact" );
{// new method for grouping, use CSR builder
std::vector<size_t> rptr;
utils::SparseCSRMBuilder<unsigned> builder( rptr, idset );
builder.InitBudget( tree.param.num_roots );
for( size_t i = 0; i < group_id.size(); i ++ ){
// drop invalid elements
if( hess[ i ] < 0.0f ) continue;
utils::Assert( group_id[ i ] < (unsigned)tree.param.num_roots,
"group id exceed number of roots" );
builder.AddBudget( group_id[ i ] );
}
builder.InitStorage();
for( size_t i = 0; i < group_id.size(); i ++ ){
// drop invalid elements
if( hess[ i ] < 0.0f ) continue;
builder.PushElem( group_id[ i ], static_cast<unsigned>(i) );
}
for( size_t i = 1; i < rptr.size(); i ++ ){
const size_t start = rptr[ i - 1 ], end = rptr[ i ];
if( start < end ){
this->add_task( Task( i - 1, &idset[ start ], end - start ) );
}
}
}
}
public:
RTreeUpdater( const TreeParamTrain &pparam,
RegTree &ptree,
std::vector<float> &pgrad,
std::vector<float> &phess,
const FMatrix &psmat,
const std::vector<unsigned> &pgroup_id ):
param( pparam ), tree( ptree ), grad( pgrad ), hess( phess ),
smat( psmat ), group_id( pgroup_id ){
}
inline int do_boost( int &num_pruned ){
this->init_tasks( grad.size() );
this->max_depth = 0;
this->num_pruned = 0;
Task tsk;
while( this->next_task( tsk ) ){
this->expand( tsk );
}
num_pruned = this->num_pruned;
return max_depth;
}
};
};
};
#endif

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booster/tree/xgboost_tree.hpp
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#ifndef XGBOOST_TREE_HPP
#define XGBOOST_TREE_HPP
/*!
* \file xgboost_tree.hpp
* \brief implementation of regression tree
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include "xgboost_tree_model.h"
namespace xgboost{
namespace booster{
const bool rt_debug = false;
// whether to check bugs
const bool check_bug = false;
const float rt_eps = 1e-5f;
const float rt_2eps = rt_eps * 2.0f;
inline double sqr( double a ){
return a * a;
}
};
};
#include "../../utils/xgboost_fmap.h"
#include "xgboost_svdf_tree.hpp"
#include "xgboost_col_treemaker.hpp"
#include "xgboost_row_treemaker.hpp"
namespace xgboost{
namespace booster{
// regression tree, construction algorithm is seperated from this class
// see RegTreeUpdater
template<typename FMatrix>
class RegTreeTrainer : public InterfaceBooster<FMatrix>{
public:
RegTreeTrainer( void ){
silent = 0; tree_maker = 1;
// interact mode
interact_type = 0;
interact_node = 0;
// normally we won't have more than 64 OpenMP threads
threadtemp.resize( 64, ThreadEntry() );
}
virtual ~RegTreeTrainer( void ){}
public:
virtual void SetParam( const char *name, const char *val ){
if( !strcmp( name, "silent") ) silent = atoi( val );
if( !strcmp( name, "tree_maker") ) tree_maker = atoi( val );
if( !strncmp( name, "interact:", 9) ){
const char *ename = name + 9;
interact_node = atoi( val );
if( !strcmp( ename, "expand") ) {
interact_type = 1;
}
if( !strcmp( ename, "remove") ) {
interact_type = 2;
}
}
param.SetParam( name, val );
constrain.SetParam( name, val );
tree.param.SetParam( name, val );
}
virtual void LoadModel( utils::IStream &fi ){
tree.LoadModel( fi );
}
virtual void SaveModel( utils::IStream &fo ) const{
tree.SaveModel( fo );
}
virtual void InitModel( void ){
tree.InitModel();
}
public:
virtual void DoBoost( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &smat,
const std::vector<unsigned> &root_index ){
utils::Assert( grad.size() < UINT_MAX, "number of instance exceed what we can handle" );
// interactive update
if( interact_type != 0 ){
switch( interact_type ){
case 1: this->ExpandNode( grad, hess, smat, root_index, interact_node ); return;
case 2: this->CollapseNode( grad, hess, smat, root_index, interact_node ); return;
default: utils::Error("unknown interact type");
}
}
if( !silent ){
printf( "\nbuild GBRT with %u instances\n", (unsigned)grad.size() );
}
int num_pruned;
switch( tree_maker ){
case 0: {
utils::Assert( !constrain.HasConstrain(), "tree maker 0 does not support constrain" );
RTreeUpdater<FMatrix> updater( param, tree, grad, hess, smat, root_index );
tree.param.max_depth = updater.do_boost( num_pruned );
break;
}
case 1:{
ColTreeMaker<FMatrix> maker( tree, param, grad, hess, smat, root_index, constrain );
maker.Make( tree.param.max_depth, num_pruned );
break;
}
case 2:{
RowTreeMaker<FMatrix> maker( tree, param, grad, hess, smat, root_index, constrain );
maker.Make( tree.param.max_depth, num_pruned );
break;
}
default: utils::Error("unknown tree maker");
}
if( !silent ){
printf( "tree train end, %d roots, %d extra nodes, %d pruned nodes ,max_depth=%d\n",
tree.param.num_roots, tree.num_extra_nodes(), num_pruned, tree.MaxDepth() );
}
}
virtual float Predict( const FMatrix &fmat, bst_uint ridx, unsigned gid = 0 ){
ThreadEntry &e = this->InitTmp();
this->PrepareTmp( fmat.GetRow(ridx), e );
int pid = this->GetLeafIndex( e.feat, e.funknown, gid );
this->DropTmp( fmat.GetRow(ridx), e );
return tree[ pid ].leaf_value();
}
virtual int GetLeafIndex( const std::vector<float> &feat,
const std::vector<bool> &funknown,
unsigned gid = 0 ){
// start from groups that belongs to current data
int pid = (int)gid;
// tranverse tree
while( !tree[ pid ].is_leaf() ){
unsigned split_index = tree[ pid ].split_index();
pid = this->GetNext( pid, feat[ split_index ], funknown[ split_index ] );
}
return pid;
}
virtual void PredPath( std::vector<int> &path, const FMatrix &fmat, bst_uint ridx, unsigned gid = 0 ){
path.clear();
ThreadEntry &e = this->InitTmp();
this->PrepareTmp( fmat.GetRow(ridx), e );
int pid = (int)gid;
path.push_back( pid );
// tranverse tree
while( !tree[ pid ].is_leaf() ){
unsigned split_index = tree[ pid ].split_index();
pid = this->GetNext( pid, e.feat[ split_index ], e.funknown[ split_index ] );
path.push_back( pid );
}
this->DropTmp( fmat.GetRow(ridx), e );
}
virtual float Predict( const std::vector<float> &feat,
const std::vector<bool> &funknown,
unsigned gid = 0 ){
utils::Assert( feat.size() >= (size_t)tree.param.num_feature,
"input data smaller than num feature" );
int pid = this->GetLeafIndex( feat, funknown, gid );
return tree[ pid ].leaf_value();
}
virtual void DumpModel( FILE *fo, const utils::FeatMap &fmap, bool with_stats ){
tree.DumpModel( fo, fmap, with_stats );
}
private:
inline void CollapseNode( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &fmat,
const std::vector<unsigned> &root_index,
int nid ){
std::vector<bst_uint> valid_index;
for( size_t i = 0; i < grad.size(); i ++ ){
ThreadEntry &e = this->InitTmp();
this->PrepareTmp( fmat.GetRow(i), e );
int pid = root_index.size() == 0 ? 0 : (int)root_index[i];
// tranverse tree
while( !tree[ pid ].is_leaf() ){
unsigned split_index = tree[ pid ].split_index();
pid = this->GetNext( pid, e.feat[ split_index ], e.funknown[ split_index ] );
if( pid == nid ){
valid_index.push_back( static_cast<bst_uint>(i) ); break;
}
}
this->DropTmp( fmat.GetRow(i), e );
}
RowTreeMaker<FMatrix> maker( tree, param, grad, hess, fmat, root_index, constrain );
maker.Collapse( valid_index, nid );
if( !silent ){
printf( "tree collapse end, max_depth=%d\n", tree.param.max_depth );
}
}
inline void ExpandNode( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &fmat,
const std::vector<unsigned> &root_index,
int nid ){
std::vector<bst_uint> valid_index;
for( size_t i = 0; i < grad.size(); i ++ ){
ThreadEntry &e = this->InitTmp();
this->PrepareTmp( fmat.GetRow(i), e );
unsigned rtidx = root_index.size() == 0 ? 0 : root_index[i];
int pid = this->GetLeafIndex( e.feat, e.funknown, rtidx );
this->DropTmp( fmat.GetRow(i), e );
if( pid == nid ) valid_index.push_back( static_cast<bst_uint>(i) );
}
RowTreeMaker<FMatrix> maker( tree, param, grad, hess, fmat, root_index, constrain );
bool success = maker.Expand( valid_index, nid );
if( !silent ){
printf( "tree expand end, success=%d, max_depth=%d\n", (int)success, tree.MaxDepth() );
}
}
private:
// silent
int silent;
RegTree tree;
TreeParamTrain param;
private:
// some training parameters
// tree maker
int tree_maker;
// interaction
int interact_type;
int interact_node;
// feature constrain
utils::FeatConstrain constrain;
private:
struct ThreadEntry{
std::vector<float> feat;
std::vector<bool> funknown;
};
std::vector<ThreadEntry> threadtemp;
private:
inline ThreadEntry& InitTmp( void ){
const int tid = omp_get_thread_num();
utils::Assert( tid < (int)threadtemp.size(), "RTreeUpdater: threadtemp pool is too small" );
ThreadEntry &e = threadtemp[ tid ];
if( e.feat.size() != (size_t)tree.param.num_feature ){
e.feat.resize( tree.param.num_feature );
e.funknown.resize( tree.param.num_feature );
std::fill( e.funknown.begin(), e.funknown.end(), true );
}
return e;
}
inline void PrepareTmp( typename FMatrix::RowIter it, ThreadEntry &e ){
while( it.Next() ){
const bst_uint findex = it.findex();
utils::Assert( findex < (unsigned)tree.param.num_feature , "input feature execeed bound" );
e.funknown[ findex ] = false;
e.feat[ findex ] = it.fvalue();
}
}
inline void DropTmp( typename FMatrix::RowIter it, ThreadEntry &e ){
while( it.Next() ){
e.funknown[ it.findex() ] = true;
}
}
inline int GetNext( int pid, float fvalue, bool is_unknown ){
float split_value = tree[ pid ].split_cond();
if( is_unknown ){
return tree[ pid ].cdefault();
}else{
if( fvalue < split_value ) return tree[ pid ].cleft();
else return tree[ pid ].cright();
}
}
};
};
};
#endif

554
booster/tree/xgboost_tree_model.h
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#ifndef XGBOOST_TREE_MODEL_H
#define XGBOOST_TREE_MODEL_H
/*!
* \file xgboost_tree_model.h
* \brief generic definition of model structure used in tree models
* used to support learning of boosting tree
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <cstring>
#include "../../utils/xgboost_utils.h"
#include "../../utils/xgboost_stream.h"
namespace xgboost{
namespace booster{
/*!
* \brief template class of TreeModel
* \tparam TSplitCond data type to indicate split condition
* \tparam TNodeStat auxiliary statistics of node to help tree building
*/
template<typename TSplitCond,typename TNodeStat>
class TreeModel{
public:
/*! \brief data type to indicate split condition */
typedef TNodeStat NodeStat;
/*! \brief auxiliary statistics of node to help tree building */
typedef TSplitCond SplitCond;
public:
/*! \brief parameters of the tree */
struct Param{
/*! \brief number of start root */
int num_roots;
/*! \brief total number of nodes */
int num_nodes;
/*!\brief number of deleted nodes */
int num_deleted;
/*! \brief maximum depth, this is a statistics of the tree */
int max_depth;
/*! \brief number of features used for tree construction */
int num_feature;
/*! \brief reserved part */
int reserved[ 32 ];
/*! \brief constructor */
Param( void ){
max_depth = 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("num_roots", name ) ) num_roots = atoi( val );
if( !strcmp("num_feature", name ) ) num_feature = atoi( val );
}
};
/*! \brief tree node */
class Node{
private:
friend class TreeModel<TSplitCond,TNodeStat>;
/*!
* \brief in leaf node, we have weights, in non-leaf nodes,
* we have split condition
*/
union Info{
float leaf_value;
TSplitCond split_cond;
};
private:
// pointer to parent, highest bit is used to indicate whether it's a left child or not
int parent_;
// pointer to left, right
int cleft_, cright_;
// split feature index, left split or right split depends on the highest bit
unsigned sindex_;
// extra info
Info info_;
private:
inline void set_parent( int pidx, bool is_left_child = true ){
if( is_left_child ) pidx |= (1U << 31);
this->parent_ = pidx;
}
public:
/*! \brief index of left child */
inline int cleft( void ) const{
return this->cleft_;
}
/*! \brief index of right child */
inline int cright( void ) const{
return this->cright_;
}
/*! \brief index of default child when feature is missing */
inline int cdefault( void ) const{
return this->default_left() ? this->cleft() : this->cright();
}
/*! \brief feature index of split condition */
inline unsigned split_index( void ) const{
return sindex_ & ( (1U<<31) - 1U );
}
/*! \brief when feature is unknown, whether goes to left child */
inline bool default_left( void ) const{
return (sindex_ >> 31) != 0;
}
/*! \brief whether current node is leaf node */
inline bool is_leaf( void ) const{
return cleft_ == -1;
}
/*! \brief get leaf value of leaf node */
inline float leaf_value( void ) const{
return (this->info_).leaf_value;
}
/*! \brief get split condition of the node */
inline TSplitCond split_cond( void ) const{
return (this->info_).split_cond;
}
/*! \brief get parent of the node */
inline int parent( void ) const{
return parent_ & ( (1U << 31) - 1 );
}
/*! \brief whether current node is left child */
inline bool is_left_child( void ) const{
return ( parent_ & (1U << 31)) != 0;
}
/*! \brief whether current node is root */
inline bool is_root( void ) const{
return parent_ == -1;
}
/*!
* \brief set the right child
* \param nide node id to right child
*/
inline void set_right_child( int nid ){
this->cright_ = nid;
}
/*!
* \brief set split condition of current node
* \param split_index feature index to split
* \param split_cond split condition
* \param default_left the default direction when feature is unknown
*/
inline void set_split( unsigned split_index, TSplitCond split_cond, bool default_left = false ){
if( default_left ) split_index |= (1U << 31);
this->sindex_ = split_index;
(this->info_).split_cond = split_cond;
}
/*!
* \brief set the leaf value of the node
* \param value leaf value
* \param right right index, could be used to store
* additional information
*/
inline void set_leaf( float value, int right = -1 ){
(this->info_).leaf_value = value;
this->cleft_ = -1;
this->cright_ = right;
}
};
protected:
// vector of nodes
std::vector<Node> nodes;
// stats of nodes
std::vector<TNodeStat> stats;
protected:
// free node space, used during training process
std::vector<int> deleted_nodes;
// allocate a new node,
// !!!!!! NOTE: may cause BUG here, nodes.resize
inline int AllocNode( void ){
if( param.num_deleted != 0 ){
int nd = deleted_nodes.back();
deleted_nodes.pop_back();
param.num_deleted --;
return nd;
}
int nd = param.num_nodes ++;
nodes.resize( param.num_nodes );
stats.resize( param.num_nodes );
return nd;
}
// delete a tree node
inline void DeleteNode( int nid ){
utils::Assert( nid >= param.num_roots, "can not delete root");
deleted_nodes.push_back( nid );
nodes[ nid ].set_parent( -1 );
param.num_deleted ++;
}
public:
/*!
* \brief change a non leaf node to a leaf node, delete its children
* \param rid node id of the node
* \param new leaf value
*/
inline void ChangeToLeaf( int rid, float value ){
utils::Assert( nodes[ nodes[rid].cleft() ].is_leaf(), "can not delete a non termial child");
utils::Assert( nodes[ nodes[rid].cright() ].is_leaf(), "can not delete a non termial child");
this->DeleteNode( nodes[ rid ].cleft() );
this->DeleteNode( nodes[ rid ].cright() );
nodes[ rid ].set_leaf( value );
}
/*!
* \brief collapse a non leaf node to a leaf node, delete its children
* \param rid node id of the node
* \param new leaf value
*/
inline void CollapseToLeaf( int rid, float value ){
if( nodes[rid].is_leaf() ) return;
if( !nodes[ nodes[rid].cleft() ].is_leaf() ){
CollapseToLeaf( nodes[rid].cleft(), 0.0f );
}
if( !nodes[ nodes[rid].cright() ].is_leaf() ){
CollapseToLeaf( nodes[rid].cright(), 0.0f );
}
this->ChangeToLeaf( rid, value );
}
public:
/*! \brief model parameter */
Param param;
public:
/*! \brief constructor */
TreeModel( void ){
param.num_nodes = 1;
param.num_roots = 1;
param.num_deleted = 0;
nodes.resize( 1 );
}
/*! \brief get node given nid */
inline Node &operator[]( int nid ){
return nodes[ nid ];
}
/*! \brief get node statistics given nid */
inline NodeStat &stat( int nid ){
return stats[ nid ];
}
/*! \brief initialize the model */
inline void InitModel( void ){
param.num_nodes = param.num_roots;
nodes.resize( param.num_nodes );
stats.resize( param.num_nodes );
for( int i = 0; i < param.num_nodes; i ++ ){
nodes[i].set_leaf( 0.0f );
nodes[i].set_parent( -1 );
}
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void LoadModel( utils::IStream &fi ){
utils::Assert( fi.Read( &param, sizeof(Param) ) > 0, "TreeModel" );
nodes.resize( param.num_nodes ); stats.resize( param.num_nodes );
utils::Assert( fi.Read( &nodes[0], sizeof(Node) * nodes.size() ) > 0, "TreeModel::Node" );
utils::Assert( fi.Read( &stats[0], sizeof(NodeStat) * stats.size() ) > 0, "TreeModel::Node" );
deleted_nodes.resize( 0 );
for( int i = param.num_roots; i < param.num_nodes; i ++ ){
if( nodes[i].is_root() ) deleted_nodes.push_back( i );
}
utils::Assert( (int)deleted_nodes.size() == param.num_deleted, "number of deleted nodes do not match" );
}
/*!
* \brief save model to stream
* \param fo output stream
*/
inline void SaveModel( utils::IStream &fo ) const{
utils::Assert( param.num_nodes == (int)nodes.size() );
utils::Assert( param.num_nodes == (int)stats.size() );
fo.Write( &param, sizeof(Param) );
fo.Write( &nodes[0], sizeof(Node) * nodes.size() );
fo.Write( &stats[0], sizeof(NodeStat) * nodes.size() );
}
/*!
* \brief add child nodes to node
* \param nid node id to add childs
*/
inline void AddChilds( int nid ){
int pleft = this->AllocNode();
int pright = this->AllocNode();
nodes[ nid ].cleft_ = pleft;
nodes[ nid ].cright_ = pright;
nodes[ nodes[ nid ].cleft() ].set_parent( nid, true );
nodes[ nodes[ nid ].cright() ].set_parent( nid, false );
}
/*!
* \brief only add a right child to a leaf node
* \param node id to add right child
*/
inline void AddRightChild( int nid ){
int pright = this->AllocNode();
nodes[ nid ].right = pright;
nodes[ nodes[ nid ].right ].set_parent( nid, false );
}
/*!
* \brief get current depth
* \param nid node id
* \param pass_rchild whether right child is not counted in depth
*/
inline int GetDepth( int nid, bool pass_rchild = false ) const{
int depth = 0;
while( !nodes[ nid ].is_root() ){
if( !pass_rchild || nodes[ nid ].is_left_child() ) depth ++;
nid = nodes[ nid ].parent();
}
return depth;
}
/*!
* \brief get maximum depth
* \param nid node id
*/
inline int MaxDepth( int nid ) const{
if( nodes[nid].is_leaf() ) return 0;
return std::max( MaxDepth( nodes[nid].cleft() )+1,
MaxDepth( nodes[nid].cright() )+1 );
}
/*!
* \brief get maximum depth
*/
inline int MaxDepth( void ){
int maxd = 0;
for( int i = 0; i < param.num_roots; ++ i ){
maxd = std::max( maxd, MaxDepth( i ) );
}
return maxd;
}
/*! \brief number of extra nodes besides the root */
inline int num_extra_nodes( void ) const {
return param.num_nodes - param.num_roots - param.num_deleted;
}
/*! \brief dump model to text file */
inline void DumpModel( FILE *fo, const utils::FeatMap& fmap, bool with_stats ){
this->Dump( 0, fo, fmap, 0, with_stats );
}
private:
void Dump( int nid, FILE *fo, const utils::FeatMap& fmap, int depth, bool with_stats ){
for( int i = 0; i < depth; ++ i ){
fprintf( fo, "\t" );
}
if( nodes[ nid ].is_leaf() ){
fprintf( fo, "%d:leaf=%f ", nid, nodes[ nid ].leaf_value() );
if( with_stats ){
stat( nid ).Print( fo, true );
}
fprintf( fo, "\n" );
}else{
// right then left,
TSplitCond cond = nodes[ nid ].split_cond();
const unsigned split_index = nodes[ nid ].split_index();
if( split_index < fmap.size() ){
switch( fmap.type(split_index) ){
case utils::FeatMap::kIndicator:{
int nyes = nodes[ nid ].default_left()?nodes[nid].cright():nodes[nid].cleft();
fprintf( fo, "%d:[%s] yes=%d,no=%d",
nid, fmap.name( split_index ),
nyes, nodes[nid].cdefault() );
break;
}
case utils::FeatMap::kInteger:{
fprintf( fo, "%d:[%s<%d] yes=%d,no=%d,missing=%d",
nid, fmap.name(split_index), int( float(cond)+1.0f),
nodes[ nid ].cleft(), nodes[ nid ].cright(),
nodes[ nid ].cdefault() );
break;
}
case utils::FeatMap::kFloat:
case utils::FeatMap::kQuantitive:{
fprintf( fo, "%d:[%s<%f] yes=%d,no=%d,missing=%d",
nid, fmap.name(split_index), float(cond),
nodes[ nid ].cleft(), nodes[ nid ].cright(),
nodes[ nid ].cdefault() );
break;
}
default: utils::Error("unknown fmap type");
}
}else{
fprintf( fo, "%d:[f%u<%f] yes=%d,no=%d,missing=%d",
nid, split_index, float(cond),
nodes[ nid ].cleft(), nodes[ nid ].cright(),
nodes[ nid ].cdefault() );
}
if( with_stats ){
fprintf( fo, " ");
stat( nid ).Print( fo, false );
}
fprintf( fo, "\n" );
this->Dump( nodes[ nid ].cleft(), fo, fmap, depth+1, with_stats );
this->Dump( nodes[ nid ].cright(), fo, fmap, depth+1, with_stats );
}
}
};
};
namespace booster{
/*! \brief training parameters for regression tree */
struct TreeParamTrain{
// learning step size for a time
float learning_rate;
// minimum loss change required for a split
float min_split_loss;
// maximum depth of a tree
int max_depth;
//----- the rest parameters are less important ----
// minimum amount of hessian(weight) allowed in a child
float min_child_weight;
// weight decay parameter used to control leaf fitting
float reg_lambda;
// reg method
int reg_method;
// default direction choice
int default_direction;
// whether we want to do subsample
float subsample;
// whether to use layerwise aware regularization
int use_layerwise;
// number of threads to be used for tree construction, if OpenMP is enabled, if equals 0, use system default
int nthread;
/*! \brief constructor */
TreeParamTrain( void ){
learning_rate = 0.3f;
min_child_weight = 1.0f;
max_depth = 6;
reg_lambda = 1.0f;
reg_method = 2;
default_direction = 0;
subsample = 1.0f;
use_layerwise = 0;
nthread = 0;
}
/*!
* \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 ){
// sync-names
if( !strcmp( name, "gamma") ) min_split_loss = (float)atof( val );
if( !strcmp( name, "eta") ) learning_rate = (float)atof( val );
if( !strcmp( name, "lambda") ) reg_lambda = (float)atof( val );
// normal tree prameters
if( !strcmp( name, "learning_rate") ) learning_rate = (float)atof( val );
if( !strcmp( name, "min_child_weight") ) min_child_weight = (float)atof( val );
if( !strcmp( name, "min_split_loss") ) min_split_loss = (float)atof( val );
if( !strcmp( name, "max_depth") ) max_depth = atoi( val );
if( !strcmp( name, "reg_lambda") ) reg_lambda = (float)atof( val );
if( !strcmp( name, "reg_method") ) reg_method = (float)atof( val );
if( !strcmp( name, "subsample") ) subsample = (float)atof( val );
if( !strcmp( name, "use_layerwise") ) use_layerwise = atoi( val );
if( !strcmp( name, "nthread") ) nthread = atoi( val );
if( !strcmp( name, "default_direction") ) {
if( !strcmp( val, "learn") ) default_direction = 0;
if( !strcmp( val, "left") ) default_direction = 1;
if( !strcmp( val, "right") ) default_direction = 2;
}
}
protected:
// functions for L1 cost
static inline double ThresholdL1( double w, double lambda ){
if( w > +lambda ) return w - lambda;
if( w < -lambda ) return w + lambda;
return 0.0;
}
inline double CalcWeight( double sum_grad, double sum_hess )const{
if( sum_hess < min_child_weight ){
return 0.0;
}else{
switch( reg_method ){
case 1: return - ThresholdL1( sum_grad, reg_lambda ) / sum_hess;
case 2: return - sum_grad / ( sum_hess + reg_lambda );
// elstic net
case 3: return - ThresholdL1( sum_grad, 0.5 * reg_lambda ) / ( sum_hess + 0.5 * reg_lambda );
default: return - sum_grad / sum_hess;
}
}
}
private:
inline static double Sqr( double a ){
return a * a;
}
public:
// calculate the cost of loss function
inline double CalcGain( double sum_grad, double sum_hess ) const{
if( sum_hess < min_child_weight ){
return 0.0;
}
switch( reg_method ){
case 1 : return Sqr( ThresholdL1( sum_grad, reg_lambda ) ) / sum_hess;
case 2 : return Sqr( sum_grad ) / ( sum_hess + reg_lambda );
// elstic net
case 3 : return Sqr( ThresholdL1( sum_grad, 0.5 * reg_lambda ) ) / ( sum_hess + 0.5 * reg_lambda );
default: return Sqr( sum_grad ) / sum_hess;
}
}
// KEY:layerwise
// calculate cost of root
inline double CalcRootGain( double sum_grad, double sum_hess ) const{
if( use_layerwise == 0 ) return this->CalcGain( sum_grad, sum_hess );
else return 0.0;
}
// KEY:layerwise
// calculate the cost after split
// base_weight: the base_weight of parent
inline double CalcGain( double sum_grad, double sum_hess, double base_weight ) const{
if( use_layerwise == 0 ) return this->CalcGain( sum_grad, sum_hess );
else return this->CalcGain( sum_grad + sum_hess * base_weight, sum_hess );
}
// calculate the weight of leaf
inline double CalcWeight( double sum_grad, double sum_hess, double parent_base_weight )const{
if( use_layerwise == 0 ) return CalcWeight( sum_grad, sum_hess );
else return parent_base_weight + CalcWeight( sum_grad + parent_base_weight * sum_hess, sum_hess );
}
/*! \brief whether need forward small to big search: default right */
inline bool need_forward_search( void ) const{
return this->default_direction != 1;
}
/*! \brief whether need forward big to small search: default left */
inline bool need_backward_search( void ) const{
return this->default_direction != 2;
}
/*! \brief given the loss change, whether we need to invode prunning */
inline bool need_prune( double loss_chg, int depth ) const{
return loss_chg < this->min_split_loss;
}
/*! \brief whether we can split with current hessian */
inline bool cannot_split( double sum_hess, int depth ) const{
return sum_hess < this->min_child_weight * 2.0;
}
};
};
namespace booster{
/*! \brief node statistics used in regression tree */
struct RTreeNodeStat{
/*! \brief loss chg caused by current split */
float loss_chg;
/*! \brief sum of hessian values, used to measure coverage of data */
float sum_hess;
/*! \brief weight of current node */
float base_weight;
/*! \brief number of child that is leaf node known up to now */
int leaf_child_cnt;
/*! \brief print information of current stats to fo */
inline void Print( FILE *fo, bool is_leaf ) const{
if( !is_leaf ){
fprintf( fo, "gain=%f,cover=%f", loss_chg, sum_hess );
}else{
fprintf( fo, "cover=%f", sum_hess );
}
}
};
/*! \brief most comment structure of regression tree */
class RegTree: public TreeModel<bst_float,RTreeNodeStat>{
};
};
};
#endif

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booster/xgboost-inl.hpp
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#ifndef XGBOOST_INL_HPP
#define XGBOOST_INL_HPP
/*!
* \file xgboost-inl.hpp
* \brief bootser implementations
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
// implementation of boosters go to here
// A good design should have minimum functions defined interface, user should only operate on interface
// I break it a bit, by using template and let user 'see' the implementation
// The user should pretend that they only can use the interface, and we are all cool
// I find this is the only way so far I can think of to make boosters invariant of data structure,
// while keep everything fast
#include "xgboost.h"
#include "../utils/xgboost_utils.h"
#include "tree/xgboost_tree.hpp"
#include "linear/xgboost_linear.hpp"
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
* \tparam FMatrix input data type for booster
* \return the pointer to the gradient booster created
*/
template<typename FMatrix>
inline InterfaceBooster<FMatrix> *CreateBooster(int booster_type){
switch (booster_type){
case 0: return new RegTreeTrainer<FMatrix>();
case 1: return new LinearBooster<FMatrix>();
default: utils::Error("unknown booster_type"); return NULL;
}
}
}; // namespace booster
}; // namespace xgboost
#endif // XGBOOST_INL_HPP

157
booster/xgboost.h
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#ifndef XGBOOST_H
#define XGBOOST_H
/*!
* \file xgboost.h
* \brief the general gradient boosting interface
*
* common practice of this header: use IBooster and CreateBooster<FMatrixS>
*
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <vector>
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_fmap.h"
#include "../utils/xgboost_stream.h"
#include "../utils/xgboost_config.h"
#include "xgboost_data.h"
/*! \brief namespace for xboost package */
namespace xgboost{
/*! \brief namespace for boosters */
namespace booster{
/*!
* \brief interface of a gradient boosting learner
* \tparam FMatrix the feature matrix format that the booster takes
*/
template<typename FMatrix>
class InterfaceBooster{
public:
// interface for model setting and loading
// calling procedure:
// (1) booster->SetParam to setting necessary parameters
// (2) if it is first time usage of the model:
// call booster->InitModel
// else:
// call booster->LoadModel
// (3) booster->DoBoost to update the model
// (4) booster->Predict to get new prediction
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
virtual void SetParam(const char *name, const char *val) = 0;
/*!
* \brief load model from stream
* \param fi input stream
*/
virtual void LoadModel(utils::IStream &fi) = 0;
/*!
* \brief save model to stream
* \param fo output stream
*/
virtual void SaveModel(utils::IStream &fo) const = 0;
/*!
* \brief initialize solver before training, called before training
* this function is reserved for solver to allocate necessary space and do other preparation
*/
virtual void InitModel(void) = 0;
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,
* root_index.size() can be 0 which indicates that no pre-partition involved
*/
virtual void DoBoost(std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &feats,
const std::vector<unsigned> &root_index) = 0;
/*!
* \brief predict the path ids along a trees, for given sparse feature vector. When booster is a tree
* \param path the result of path
* \param feats feature matrix
* \param row_index row index in the feature matrix
* \param root_index root id of current instance, default = 0
*/
virtual void PredPath(std::vector<int> &path, const FMatrix &feats,
bst_uint row_index, unsigned root_index = 0){
utils::Error("not implemented");
}
/*!
* \brief predict values for given sparse feature vector
*
* NOTE: in tree implementation, Sparse Predict is OpenMP threadsafe, but not threadsafe in general,
* dense version of Predict to ensures threadsafety
* \param feats feature matrix
* \param row_index row index in the feature matrix
* \param root_index root id of current instance, default = 0
* \return prediction
*/
virtual float Predict(const FMatrix &feats, bst_uint row_index, unsigned root_index = 0){
utils::Error("not implemented");
return 0.0f;
}
/*!
* \brief predict values for given dense feature vector
* \param feat feature vector in dense format
* \param funknown indicator that the feature is missing
* \param rid root id of current instance, default = 0
* \return prediction
*/
virtual float Predict(const std::vector<float> &feat,
const std::vector<bool> &funknown,
unsigned rid = 0){
utils::Error("not implemented");
return 0.0f;
}
/*!
* \brief print information
* \param fo output stream
*/
virtual void PrintInfo(FILE *fo){}
/*!
* \brief dump model into text file
* \param fo output stream
* \param fmap feature map that may help give interpretations of feature
* \param with_stats whether print statistics
*/
virtual void DumpModel(FILE *fo, const utils::FeatMap& fmap, bool with_stats = false){
utils::Error("not implemented");
}
public:
/*! \brief virtual destructor */
virtual ~InterfaceBooster(void){}
};
};
namespace booster{
/*!
* \brief this will is the most commonly used booster interface
* we try to make booster invariant of data structures, but most cases, FMatrixS is what we wnat
*/
typedef InterfaceBooster<FMatrixS> IBooster;
};
};
namespace xgboost{
namespace booster{
/*!
* \brief create a gradient booster, given type of booster
* normally we use FMatrixS, by calling CreateBooster<FMatrixS>
* \param booster_type type of gradient booster, can be used to specify implements
* \tparam FMatrix input data type for booster
* \return the pointer to the gradient booster created
*/
template<typename FMatrix>
inline InterfaceBooster<FMatrix> *CreateBooster(int booster_type);
};
};
// this file includes the template implementations of all boosters
// the cost of using template is that the user can 'see' all the implementations, which is OK
// ignore implementations and focus on the interface:)
#include "xgboost-inl.hpp"
#endif

396
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#ifndef XGBOOST_DATA_H
#define XGBOOST_DATA_H
/*!
* \file xgboost_data.h
* \brief the input data structure for gradient boosting
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <vector>
#include <climits>
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_stream.h"
#include "../utils/xgboost_matrix_csr.h"
namespace xgboost{
namespace booster{
/*! \brief interger type used in boost */
typedef int bst_int;
/*! \brief unsigned interger type used in boost */
typedef unsigned bst_uint;
/*! \brief float type used in boost */
typedef float bst_float;
/*! \brief debug option for booster */
const bool bst_debug = false;
};
};
namespace xgboost{
namespace booster{
/**
* \brief This is a interface, defining the way to access features,
* by column or by row. This interface is used to make implementation
* of booster does not depend on how feature is stored.
*
* Why template instead of virtual class: for efficiency
* feature matrix is going to be used by most inner loop of the algorithm
*
* \tparam Derived type of actual implementation
* \sa FMatrixS: most of time FMatrixS is sufficient, refer to it if you find it confusing
*/
template<typename Derived>
struct FMatrix{
public:
/*! \brief exmaple iterator over one row */
struct RowIter{
/*!
* \brief move to next position
* \return whether there is element in next position
*/
inline bool Next(void);
/*! \return feature index in current position */
inline bst_uint findex(void) const;
/*! \return feature value in current position */
inline bst_float fvalue(void) const;
};
/*! \brief example iterator over one column */
struct ColIter{
/*!
* \brief move to next position
* \return whether there is element in next position
*/
inline bool Next(void);
/*! \return row index of current position */
inline bst_uint rindex(void) const;
/*! \return feature value in current position */
inline bst_float fvalue(void) const;
};
/*! \brief backward iterator over column */
struct ColBackIter : public ColIter {};
public:
/*!
* \brief get number of rows
* \return number of rows
*/
inline size_t NumRow(void) const;
/*!
* \brief get number of columns
* \return number of columns
*/
inline size_t NumCol(void) const;
/*!
* \brief get row iterator
* \param ridx row index
* \return row iterator
*/
inline RowIter GetRow(size_t ridx) const;
/*!
* \brief get number of column groups, this ise used together with GetRow( ridx, gid )
* \return number of column group
*/
inline unsigned NumColGroup(void) const{
return 1;
}
/*!
* \brief get row iterator, return iterator of specific column group
* \param ridx row index
* \param gid colmun group id
* \return row iterator, only iterates over features of specified column group
*/
inline RowIter GetRow(size_t ridx, unsigned gid) const;
/*! \return whether column access is enabled */
inline bool HaveColAccess(void) const;
/*!
* \brief get column iterator, the columns must be sorted by feature value
* \param ridx column index
* \return column iterator
*/
inline ColIter GetSortedCol(size_t ridx) const;
/*!
* \brief get column backward iterator, starts from biggest fvalue, and iterator back
* \param ridx column index
* \return reverse column iterator
*/
inline ColBackIter GetReverseSortedCol(size_t ridx) const;
};
};
};
namespace xgboost{
namespace booster{
/*!
* \brief feature matrix to store training instance, in sparse CSR format
*/
class FMatrixS : public FMatrix<FMatrixS>{
public:
/*! \brief one entry in a row */
struct REntry{
/*! \brief feature index */
bst_uint findex;
/*! \brief feature value */
bst_float fvalue;
/*! \brief constructor */
REntry(void){}
/*! \brief constructor */
REntry(bst_uint findex, bst_float fvalue) : findex(findex), fvalue(fvalue){}
inline static bool cmp_fvalue(const REntry &a, const REntry &b){
return a.fvalue < b.fvalue;
}
};
/*! \brief one row of sparse feature matrix */
struct Line{
/*! \brief array of feature index */
const REntry *data_;
/*! \brief size of the data */
bst_uint len;
/*! \brief get k-th element */
inline const REntry& operator[](unsigned i) const{
return data_[i];
}
};
/*! \brief row iterator */
struct RowIter{
const REntry *dptr_, *end_;
RowIter(const REntry* dptr, const REntry* end)
:dptr_(dptr), end_(end){}
inline bool Next(void){
if (dptr_ == end_) return false;
else{
++dptr_; return true;
}
}
inline bst_uint findex(void) const{
return dptr_->findex;
}
inline bst_float fvalue(void) const{
return dptr_->fvalue;
}
};
/*! \brief column iterator */
struct ColIter : public RowIter{
ColIter(const REntry* dptr, const REntry* end)
:RowIter(dptr, end){}
inline bst_uint rindex(void) const{
return this->findex();
}
};
/*! \brief reverse column iterator */
struct ColBackIter : public ColIter{
ColBackIter(const REntry* dptr, const REntry* end)
:ColIter(dptr, end){}
// shadows RowIter::Next
inline bool Next(void){
if (dptr_ == end_) return false;
else{
--dptr_; return true;
}
}
};
public:
/*! \brief constructor */
FMatrixS(void){ this->Clear(); }
/*! \brief get number of rows */
inline size_t NumRow(void) const{
return row_ptr_.size() - 1;
}
/*!
* \brief get number of nonzero entries
* \return number of nonzero entries
*/
inline size_t NumEntry(void) const{
return row_data_.size();
}
/*! \brief clear the storage */
inline void Clear(void){
row_ptr_.clear();
row_ptr_.push_back(0);
row_data_.clear();
col_ptr_.clear();
col_data_.clear();
}
/*! \brief get sparse part of current row */
inline Line operator[](size_t sidx) const{
Line sp;
utils::Assert(!bst_debug || sidx < this->NumRow(), "row id exceed bound");
sp.len = static_cast<bst_uint>(row_ptr_[sidx + 1] - row_ptr_[sidx]);
sp.data_ = &row_data_[row_ptr_[sidx]];
return sp;
}
/*!
* \brief add a row to the matrix, with data stored in STL container
* \param findex feature index
* \param fvalue feature value
* \param fstart start bound of feature
* \param fend end bound range of feature
* \return the row id added line
*/
inline size_t AddRow(const std::vector<bst_uint> &findex,
const std::vector<bst_float> &fvalue,
unsigned fstart = 0, unsigned fend = UINT_MAX){
utils::Assert(findex.size() == fvalue.size());
unsigned cnt = 0;
for (size_t i = 0; i < findex.size(); i++){
if (findex[i] < fstart || findex[i] >= fend) continue;
row_data_.push_back(REntry(findex[i], fvalue[i]));
cnt++;
}
row_ptr_.push_back(row_ptr_.back() + cnt);
return row_ptr_.size() - 2;
}
/*! \brief get row iterator*/
inline RowIter GetRow(size_t ridx) const{
utils::Assert(!bst_debug || ridx < this->NumRow(), "row id exceed bound");
return RowIter(&row_data_[row_ptr_[ridx]] - 1, &row_data_[row_ptr_[ridx + 1]] - 1);
}
/*! \brief get row iterator*/
inline RowIter GetRow(size_t ridx, unsigned gid) const{
utils::Assert(gid == 0, "FMatrixS only have 1 column group");
return FMatrixS::GetRow(ridx);
}
public:
/*! \return whether column access is enabled */
inline bool HaveColAccess(void) const{
return col_ptr_.size() != 0 && col_data_.size() == row_data_.size();
}
/*! \brief get number of colmuns */
inline size_t NumCol(void) const{
utils::Assert(this->HaveColAccess());
return col_ptr_.size() - 1;
}
/*! \brief get col iterator*/
inline ColIter GetSortedCol(size_t cidx) const{
utils::Assert(!bst_debug || cidx < this->NumCol(), "col id exceed bound");
return ColIter(&col_data_[col_ptr_[cidx]] - 1, &col_data_[col_ptr_[cidx + 1]] - 1);
}
/*! \brief get col iterator */
inline ColBackIter GetReverseSortedCol(size_t cidx) const{
utils::Assert(!bst_debug || cidx < this->NumCol(), "col id exceed bound");
return ColBackIter(&col_data_[col_ptr_[cidx + 1]], &col_data_[col_ptr_[cidx]]);
}
/*!
* \brief intialize the data so that we have both column and row major
* access, call this whenever we need column access
*/
inline void InitData(void){
utils::SparseCSRMBuilder<REntry> builder(col_ptr_, col_data_);
builder.InitBudget(0);
for (size_t i = 0; i < this->NumRow(); i++){
for (RowIter it = this->GetRow(i); it.Next();){
builder.AddBudget(it.findex());
}
}
builder.InitStorage();
for (size_t i = 0; i < this->NumRow(); i++){
for (RowIter it = this->GetRow(i); it.Next();){
builder.PushElem(it.findex(), REntry((bst_uint)i, it.fvalue()));
}
}
// sort columns
unsigned ncol = static_cast<unsigned>(this->NumCol());
#pragma omp parallel for schedule(static)
for (unsigned i = 0; i < ncol; i++){
std::sort(&col_data_[col_ptr_[i]], &col_data_[col_ptr_[i + 1]], REntry::cmp_fvalue);
}
}
/*!
* \brief save data to binary stream
* note: since we have size_t in ptr,
* the function is not consistent between 64bit and 32bit machine
* \param fo output stream
*/
inline void SaveBinary(utils::IStream &fo) const{
FMatrixS::SaveBinary(fo, row_ptr_, row_data_);
int col_access = this->HaveColAccess() ? 1 : 0;
fo.Write(&col_access, sizeof(int));
if (col_access != 0){
FMatrixS::SaveBinary(fo, col_ptr_, col_data_);
}
}
/*!
* \brief load data from binary stream
* note: since we have size_t in ptr,
* the function is not consistent between 64bit and 32bit machin
* \param fi input stream
*/
inline void LoadBinary(utils::IStream &fi){
FMatrixS::LoadBinary(fi, row_ptr_, row_data_);
int col_access;
fi.Read(&col_access, sizeof(int));
if (col_access != 0){
FMatrixS::LoadBinary(fi, col_ptr_, col_data_);
}else{
this->InitData();
}
}
/*!
* \brief load from text file
* \param fi input file pointer
*/
inline void LoadText(FILE *fi){
this->Clear();
int ninst;
while (fscanf(fi, "%d", &ninst) == 1){
std::vector<booster::bst_uint> findex;
std::vector<booster::bst_float> fvalue;
while (ninst--){
unsigned index; float value;
utils::Assert(fscanf(fi, "%u:%f", &index, &value) == 2, "load Text");
findex.push_back(index); fvalue.push_back(value);
}
this->AddRow(findex, fvalue);
}
// initialize column support as well
this->InitData();
}
private:
/*!
* \brief save data to binary stream
* \param fo output stream
* \param ptr pointer data
* \param data data content
*/
inline static void SaveBinary(utils::IStream &fo,
const std::vector<size_t> &ptr,
const std::vector<REntry> &data){
size_t nrow = ptr.size() - 1;
fo.Write(&nrow, sizeof(size_t));
fo.Write(&ptr[0], ptr.size() * sizeof(size_t));
if (data.size() != 0){
fo.Write(&data[0], data.size() * sizeof(REntry));
}
}
/*!
* \brief load data from binary stream
* \param fi input stream
* \param ptr pointer data
* \param data data content
*/
inline static void LoadBinary(utils::IStream &fi,
std::vector<size_t> &ptr,
std::vector<REntry> &data){
size_t nrow;
utils::Assert(fi.Read(&nrow, sizeof(size_t)) != 0, "Load FMatrixS");
ptr.resize(nrow + 1);
utils::Assert(fi.Read(&ptr[0], ptr.size() * sizeof(size_t)) != 0, "Load FMatrixS");
data.resize(ptr.back());
if (data.size() != 0){
utils::Assert(fi.Read(&data[0], data.size() * sizeof(REntry)) != 0, "Load FMatrixS");
}
}
public:
/*! \brief row pointer of CSR sparse storage */
std::vector<size_t> row_ptr_;
/*! \brief data in the row */
std::vector<REntry> row_data_;
/*! \brief column pointer of CSC format */
std::vector<size_t> col_ptr_;
/*! \brief column datas */
std::vector<REntry> col_data_;
};
};
};
#endif

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#ifndef XGBOOST_GBMBASE_H
#define XGBOOST_GBMBASE_H
#include <cstring>
#include "xgboost.h"
#include "xgboost_data.h"
#include "../utils/xgboost_omp.h"
#include "../utils/xgboost_config.h"
/*!
* \file xgboost_gbmbase.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
*
* 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,
* 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
* *
* 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
* ensembls the boosters together;
* (2) GBMBaseModel provides prediction buffering scheme to speedup training;
* (3) Summary: GBMBaseModel is a standard wrapper for boosting ensembles;
*
* Usage of this class, the number index gives calling dependencies:
* (1) model.SetParam to set the parameters
* (2) model.LoadModel to load old models or model.InitModel to create a new model
* (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
*
* 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
* 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){}
/*! \brief destructor */
virtual ~GBMBase(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)){
cfg.PushBack(name + 4, val);
}
if (!strcmp(name, "silent")){
cfg.PushBack(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);
}
{// 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);
}
}
if (mparam.num_pbuffer != 0){
pred_buffer.resize(mparam.PredBufferSize());
pred_counter.resize(mparam.PredBufferSize());
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);
}
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));
}
}
/*!
* \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(mparam.PredBufferSize(), 0.0);
pred_counter.resize(mparam.PredBufferSize(), 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);
}
if (mparam.num_booster_group == 0) mparam.num_booster_group = 1;
// make sure all the boosters get the latest parameters
for (size_t i = 0; i < this->boosters.size(); i++){
this->ConfigBooster(this->boosters[i]);
}
}
/*!
* \brief DumpModel
* \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);
}
}
/*!
* \brief Dump path of all trees
* \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");
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]);
}
}
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,
* root_index.size() can be 0 which indicates that no pre-partition involved
* \param bst_group which booster group it belongs to, by default, we only have 1 booster group, and leave this parameter as default
*/
inline void DoBoost(std::vector<float> &grad,
std::vector<float> &hess,
const booster::FMatrixS &feats,
const std::vector<unsigned> &root_index,
int bst_group = 0 ) {
booster::IBooster *bst = this->GetUpdateBooster( bst_group );
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
* \param bst_group booster group index
* \return prediction
*/
inline float Predict(const FMatrixS &feats, bst_uint row_index,
int buffer_index = -1, unsigned root_index = 0, int bst_group = 0 ){
size_t itop = 0;
float psum = 0.0f;
const int bid = mparam.BufferOffset(buffer_index, bst_group);
// load buffered results if any
if (mparam.do_reboost == 0 && bid >= 0){
itop = this->pred_counter[bid];
psum = this->pred_buffer[bid];
}
for (size_t i = itop; i < this->boosters.size(); ++i ){
if( booster_info[i] == bst_group ){
psum += this->boosters[i]->Predict(feats, row_index, root_index);
}
}
// updated the buffered results
if (mparam.do_reboost == 0 && bid >= 0){
this->pred_counter[bid] = static_cast<unsigned>(boosters.size());
this->pred_buffer[bid] = psum;
}
return psum;
}
/*! \return number of boosters so far */
inline int NumBoosters(void) const{
return mparam.num_boosters;
}
/*! \return number of booster groups */
inline int NumBoosterGroup(void) const{
if( mparam.num_booster_group == 0 ) return 1;
return mparam.num_booster_group;
}
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
* 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, int bst_group = 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");
if( bst_group == booster_info[bid] ){
psum -= boosters[bid]->Predict(feats, row_index, root_index);
}
if (mparam.do_reboost == 0 && buffer_index >= 0){
this->pred_buffer[mparam.BufferOffset(buffer_index,bst_group)] = psum;
}
}
return psum;
}
/*! \brief delete the specified booster */
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());
// update pred counter
for( size_t i = 0; i < pred_counter.size(); ++ i ){
if( pred_counter[i] > (unsigned)bid ) pred_counter[i] -= 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, int bst_group = 0 ){
if (tparam.reupdate_booster != -1){
const int bid = tparam.reupdate_booster;
if( booster_info[bid] != bst_group ) return;
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[mparam.BufferOffset(buffer_index,bst_group)] += 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++){
delete boosters[i];
}
boosters.clear(); booster_info.clear(); mparam.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(int bst_group){
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]);
utils::Assert( bst_group == booster_info[bid], "booster group must match existing reupdate booster");
return boosters[bid];
}
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(bst_group);
this->ConfigBooster(boosters.back());
boosters.back()->InitModel();
}
else{
this->ConfigBooster(boosters.back());
}
return boosters.back();
}
protected:
/*! \brief model parameters */
struct ModelParam{
/*! \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_roots;
/*! \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 number of booster group, how many predictions a single
* input instance could corresponds to
*/
int num_booster_group;
/*! \brief reserved parameters */
int reserved[31];
/*! \brief constructor */
ModelParam(void){
num_boosters = 0;
booster_type = 0;
num_roots = num_feature = 0;
do_reboost = 0;
num_pbuffer = 0;
num_booster_group = 1;
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);
// linear boost automatically set do reboost
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("num_booster_group", name)) num_booster_group = atoi(val);
if (!strcmp("bst:num_roots", name)) num_roots = atoi(val);
if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
}
inline int PredBufferSize(void) const{
if (num_booster_group == 0) return num_pbuffer;
else return num_booster_group * num_pbuffer;
}
inline int BufferOffset( int buffer_index, int bst_group ) const{
if( buffer_index < 0 ) return -1;
utils::Assert( buffer_index < num_pbuffer, "buffer_indexexceed num_pbuffer" );
return buffer_index + num_pbuffer * bst_group;
}
};
/*! \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
* parameter this is part of trial interactive update mode
*/
int reupdate_booster;
/*! \brief constructor */
TrainParam(void) {
nthread = 1;
reupdate_booster = -1;
}
/*!
* \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);
}
};
protected:
/*! \brief model parameters */
ModelParam mparam;
/*! \brief training parameters */
TrainParam tparam;
protected:
/*! \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

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#ifndef XGBOOST_UNITY_DATA_H
#define XGBOOST_UNITY_DATA_H
/*!
* \file data.h
* \brief the input data structure for gradient boosting
* \author Tianqi Chen
*/
#include <cstdio>
#include <vector>
#include <limits>
#include <algorithm>
#include "utils/io.h"
#include "utils/utils.h"
#include "utils/iterator.h"
#include "utils/matrix_csr.h"
namespace xgboost {
/*!
* \brief unsigned interger type used in boost,
* used for feature index and row index
*/
typedef unsigned bst_uint;
/*! \brief float type, used for storing statistics */
typedef float bst_float;
const float rt_eps = 1e-5f;
// min gap between feature values to allow a split happen
const float rt_2eps = rt_eps * 2.0f;
/*! \brief gradient statistics pair usually needed in gradient boosting */
struct bst_gpair{
/*! \brief gradient statistics */
bst_float grad;
/*! \brief second order gradient statistics */
bst_float hess;
bst_gpair(void) {}
bst_gpair(bst_float grad, bst_float hess) : grad(grad), hess(hess) {}
};
/*! \brief read-only sparse instance batch in CSR format */
struct SparseBatch {
/*! \brief an entry of sparse vector */
struct Entry {
/*! \brief feature index */
bst_uint findex;
/*! \brief feature value */
bst_float fvalue;
// default constructor
Entry(void) {}
Entry(bst_uint findex, bst_float fvalue) : findex(findex), fvalue(fvalue) {}
/*! \brief reversely compare feature values */
inline static bool CmpValue(const Entry &a, const Entry &b) {
return a.fvalue < b.fvalue;
}
};
/*! \brief an instance of sparse vector in the batch */
struct Inst {
/*! \brief pointer to the elements*/
const Entry *data;
/*! \brief length of the instance */
const bst_uint length;
/*! \brief constructor */
Inst(const Entry *data, bst_uint length) : data(data), length(length) {}
/*! \brief get i-th pair in the sparse vector*/
inline const Entry& operator[](size_t i) const {
return data[i];
}
};
/*! \brief batch size */
size_t size;
/*! \brief the offset of rowid of this batch */
size_t base_rowid;
/*! \brief array[size+1], row pointer of each of the elements */
const size_t *row_ptr;
/*! \brief array[row_ptr.back()], content of the sparse element */
const Entry *data_ptr;
/*! \brief get i-th row from the batch */
inline Inst operator[](size_t i) const {
return Inst(data_ptr + row_ptr[i], row_ptr[i+1] - row_ptr[i]);
}
};
/**
* \brief This is a interface convention via template, defining the way to access features,
* column access rule is defined by template, for efficiency purpose,
* row access is defined by iterator of sparse batches
* \tparam Derived type of actual implementation
*/
template<typename Derived>
class FMatrixInterface {
public:
/*! \brief example iterator over one column */
struct ColIter{
/*!
* \brief move to next position
* \return whether there is element in next position
*/
inline bool Next(void);
/*! \return row index of current position */
inline bst_uint rindex(void) const;
/*! \return feature value in current position */
inline bst_float fvalue(void) const;
};
/*! \brief backward iterator over column */
struct ColBackIter : public ColIter {};
public:
// column access is needed by some of tree construction algorithms
/*!
* \brief get column iterator, the columns must be sorted by feature value
* \param cidx column index
* \return column iterator
*/
inline ColIter GetSortedCol(size_t cidx) const;
/*!
* \brief get column backward iterator, starts from biggest fvalue, and iterator back
* \param cidx column index
* \return reverse column iterator
*/
inline ColBackIter GetReverseSortedCol(size_t cidx) const;
/*!
* \brief get number of columns
* \return number of columns
*/
inline size_t NumCol(void) const;
/*!
* \brief check if column access is supported, if not, initialize column access
* \param max_rows maximum number of rows allowed in constructor
*/
inline void InitColAccess(void);
/*! \return whether column access is enabled */
inline bool HaveColAccess(void) const;
/*! \breif return #entries-in-col */
inline size_t GetColSize(size_t cidx) const;
/*!
* \breif return #entries-in-col / #rows
* \param cidx column index
* this function is used to help speedup,
* doese not necessarily implement it if not sure, return 0.0;
* \return column density
*/
inline float GetColDensity(size_t cidx) const;
/*! \brief get the row iterator associated with FMatrix */
virtual utils::IIterator<SparseBatch>* RowIterator(void) const = 0;
};
/*!
* \brief sparse matrix that support column access, CSC
*/
class FMatrixS : public FMatrixInterface<FMatrixS>{
public:
typedef SparseBatch::Entry Entry;
/*! \brief row iterator */
struct ColIter{
const Entry *dptr_, *end_;
ColIter(const Entry* begin, const Entry* end)
:dptr_(begin), end_(end) {}
inline bool Next(void) {
if (dptr_ == end_) {
return false;
} else {
++dptr_; return true;
}
}
inline bst_uint rindex(void) const {
return dptr_->findex;
}
inline bst_float fvalue(void) const {
return dptr_->fvalue;
}
};
/*! \brief reverse column iterator */
struct ColBackIter : public ColIter {
ColBackIter(const Entry* dptr, const Entry* end) : ColIter(dptr, end) {}
// shadows ColIter::Next
inline bool Next(void) {
if (dptr_ == end_) {
return false;
} else {
--dptr_; return true;
}
}
};
/*! \brief constructor */
explicit FMatrixS(utils::IIterator<SparseBatch> *base_iter)
: iter_(base_iter) {}
// destructor
virtual ~FMatrixS(void) {
delete iter_;
}
/*! \return whether column access is enabled */
inline bool HaveColAccess(void) const {
return col_ptr_.size() != 0;
}
/*! \brief get number of colmuns */
inline size_t NumCol(void) const {
utils::Check(this->HaveColAccess(), "NumCol:need column access");
return col_ptr_.size() - 1;
}
/*! \brief get col sorted iterator */
inline ColIter GetSortedCol(size_t cidx) const {
utils::Assert(cidx < this->NumCol(), "col id exceed bound");
return ColIter(&col_data_[col_ptr_[cidx]] - 1,
&col_data_[col_ptr_[cidx + 1]] - 1);
}
/*!
* \brief get reversed col iterator,
* this function will be deprecated at some point
*/
inline ColBackIter GetReverseSortedCol(size_t cidx) const {
utils::Assert(cidx < this->NumCol(), "col id exceed bound");
return ColBackIter(&col_data_[col_ptr_[cidx + 1]],
&col_data_[col_ptr_[cidx]]);
}
/*! \brief get col size */
inline size_t GetColSize(size_t cidx) const {
return col_ptr_[cidx+1] - col_ptr_[cidx];
}
/*! \brief get column density */
inline float GetColDensity(size_t cidx) const {
size_t nmiss = num_buffered_row_ - (col_ptr_[cidx+1] - col_ptr_[cidx]);
return 1.0f - (static_cast<float>(nmiss)) / num_buffered_row_;
}
virtual void InitColAccess(void) {
if (this->HaveColAccess()) return;
const size_t max_nrow = std::numeric_limits<bst_uint>::max();
this->InitColData(max_nrow);
}
/*! \brief get the row iterator associated with FMatrix */
virtual utils::IIterator<SparseBatch>* RowIterator(void) const {
return iter_;
}
protected:
/*!
* \brief intialize column data
* \param max_nrow maximum number of rows supported
*/
inline void InitColData(size_t max_nrow) {
// note: this part of code is serial, todo, parallelize this transformer
utils::SparseCSRMBuilder<SparseBatch::Entry> builder(col_ptr_, col_data_);
builder.InitBudget(0);
// start working
iter_->BeforeFirst();
num_buffered_row_ = 0;
while (iter_->Next()) {
const SparseBatch &batch = iter_->Value();
if (batch.base_rowid >= max_nrow) break;
const size_t nbatch = std::min(batch.size, max_nrow - batch.base_rowid);
for (size_t i = 0; i < nbatch; ++i, ++num_buffered_row_) {
SparseBatch::Inst inst = batch[i];
for (bst_uint j = 0; j < batch.size; ++j) {
builder.AddBudget(inst[j].findex);
}
}
}
builder.InitStorage();
iter_->BeforeFirst();
while (iter_->Next()) {
const SparseBatch &batch = iter_->Value();
if (batch.base_rowid >= max_nrow) break;
const size_t nbatch = std::min(batch.size, max_nrow - batch.base_rowid);
for (size_t i = 0; i < nbatch; ++i) {
SparseBatch::Inst inst = batch[i];
for (bst_uint j = 0; j < batch.size; ++j) {
builder.PushElem(inst[j].findex,
Entry((bst_uint)(batch.base_rowid+j),
inst[j].fvalue));
}
}
}
// sort columns
unsigned ncol = static_cast<unsigned>(this->NumCol());
#pragma omp parallel for schedule(static)
for (unsigned i = 0; i < ncol; ++i) {
std::sort(&col_data_[col_ptr_[i]],
&col_data_[col_ptr_[i + 1]], Entry::CmpValue);
}
}
private:
// --- data structure used to support InitColAccess --
utils::IIterator<SparseBatch> *iter_;
/*! \brief number */
size_t num_buffered_row_;
/*! \brief column pointer of CSC format */
std::vector<size_t> col_ptr_;
/*! \brief column datas in CSC format */
std::vector<SparseBatch::Entry> col_data_;
};
} // namespace xgboost
#endif

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#ifndef XGBOOST_GBM_GBM_H_
#define XGBOOST_GBM_GBM_H_
/*!
* \file gbm.h
* \brief interface of gradient booster, that learns through gradient statistics
* \author Tianqi Chen
*/
#include <vector>
#include "../data.h"
namespace xgboost {
/*! \brief namespace for gradient booster */
namespace gbm {
/*!
* \brief interface of gradient boosting model
* \tparam FMatrix the data type updater taking
*/
template<typename FMatrix>
class IGradBooster {
public:
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
virtual void SetParam(const char *name, const char *val) = 0;
/*!
* \brief load model from stream
* \param fi input stream
*/
virtual void LoadModel(utils::IStream &fi) = 0;
/*!
* \brief save model to stream
* \param fo output stream
*/
virtual void SaveModel(utils::IStream &fo) const = 0;
/*!
* \brief initialize the model
*/
virtual void InitModel(void) = 0;
/*!
* \brief peform update to the model(boosting)
* \param gpair the gradient pair statistics of the data
* \param fmat feature matrix that provide access to features
* \param root_index pre-partitioned root_index of each instance,
* root_index.size() can be 0 which indicates that no pre-partition involved
*/
virtual void DoBoost(const std::vector<bst_gpair> &gpair,
FMatrix &fmat,
const std::vector<unsigned> &root_index) = 0;
/*!
* \brief generate predictions for given feature matrix
* \param fmat feature matrix
* \param buffer_offset buffer index offset of these instances, if equals -1
* this means we do not have buffer index allocated to the gbm
* a buffer index is assigned to each instance that requires repeative prediction
* the size of buffer is set by convention using IGradBooster.SetParam("num_pbuffer","size")
* \param root_index pre-partitioned root_index of each instance,
* root_index.size() can be 0 which indicates that no pre-partition involved
* \param out_preds output vector to hold the predictions
*/
virtual void Predict(const FMatrix &fmat,
int64_t buffer_offset,
const std::vector<unsigned> &root_index,
std::vector<float> *out_preds) = 0;
// destrcutor
virtual ~IGradBooster(void){}
};
} // namespace gbm
} // namespace xgboost
#include "gbtree-inl.hpp"
namespace xgboost {
namespace gbm {
template<typename FMatrix>
inline IGradBooster<FMatrix>* CreateGradBooster(const char *name) {
if (!strcmp("gbtree", name)) return new GBTree<FMatrix>();
utils::Error("unknown booster type: %s", name);
return NULL;
}
} // namespace gbm
} // namespace xgboost
#endif // XGBOOST_GBM_GBM_H_

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#ifndef XGBOOST_GBM_GBTREE_INL_HPP_
#define XGBOOST_GBM_GBTREE_INL_HPP_
/*!
* \file gbtree-inl.hpp
* \brief gradient boosted tree implementation
* \author Tianqi Chen
*/
#include <vector>
#include <utility>
#include <string>
#include "./gbm.h"
#include "../tree/updater.h"
namespace xgboost {
namespace gbm {
/*!
* \brief gradient boosted tree
* \tparam FMatrix the data type updater taking
*/
template<typename FMatrix>
class GBTree : public IGradBooster<FMatrix> {
public:
virtual ~GBTree(void) {
this->Clear();
}
virtual void SetParam(const char *name, const char *val) {
if (!strncmp(name, "bst:", 4)) {
cfg.push_back(std::make_pair(std::string(name+4), std::string(val)));
// set into updaters, if already intialized
for (size_t i = 0; i < updaters.size(); ++i) {
updaters[i]->SetParam(name+4, val);
}
}
if (!strcmp(name, "silent")) {
this->SetParam("bst:silent", val);
}
tparam.SetParam(name, val);
if (trees.size() == 0) mparam.SetParam(name, val);
}
virtual void LoadModel(utils::IStream &fi) {
this->Clear();
utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
"GBTree: invalid model file");
trees.resize(mparam.num_trees);
for (size_t i = 0; i < trees.size(); ++i) {
trees[i] = new tree::RegTree();
trees[i]->LoadModel(fi);
}
tree_info.resize(mparam.num_trees);
if (mparam.num_trees != 0) {
utils::Check(fi.Read(&tree_info[0], sizeof(int) * mparam.num_trees) != 0,
"GBTree: invalid model file");
}
if (mparam.num_pbuffer != 0) {
pred_buffer.resize(mparam.PredBufferSize());
pred_counter.resize(mparam.PredBufferSize());
utils::Check(fi.Read(&pred_buffer[0], pred_buffer.size() * sizeof(float)) != 0,
"GBTree: invalid model file");
utils::Check(fi.Read(&pred_counter[0], pred_counter.size() * sizeof(unsigned)) != 0,
"GBTree: invalid model file");
}
}
virtual void SaveModel(utils::IStream &fo) const {
utils::Assert(mparam.num_trees == static_cast<int>(trees.size()), "GBTree");
fo.Write(&mparam, sizeof(ModelParam));
for (size_t i = 0; i < trees.size(); ++i) {
trees[i]->SaveModel(fo);
}
if (tree_info.size() != 0) {
fo.Write(&tree_info[0], sizeof(int) * tree_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));
}
}
// initialize the predic buffer
virtual void InitModel(void) {
pred_buffer.clear(); pred_counter.clear();
pred_buffer.resize(mparam.PredBufferSize(), 0.0f);
pred_counter.resize(mparam.PredBufferSize(), 0);
utils::Assert(mparam.num_trees == 0, "GBTree: model already initialized");
utils::Assert(trees.size() == 0, "GBTree: model already initialized");
}
virtual void DoBoost(const std::vector<bst_gpair> &gpair,
FMatrix &fmat,
const std::vector<unsigned> &root_index) {
if (mparam.num_output_group == 1) {
this->BoostNewTrees(gpair, fmat, root_index, 0);
} else {
const int ngroup = mparam.num_output_group;
utils::Check(gpair.size() % ngroup == 0,
"must have exactly ngroup*nrow gpairs");
std::vector<bst_gpair> tmp(gpair.size()/ngroup);
for (int gid = 0; gid < ngroup; ++gid) {
#pragma omp parallel for schedule(static)
for (size_t i = 0; i < tmp.size(); ++i) {
tmp[i] = gpair[i * ngroup + gid];
}
this->BoostNewTrees(tmp, fmat, root_index, gid);
}
}
}
virtual void Predict(const FMatrix &fmat,
int64_t buffer_offset,
const std::vector<unsigned> &root_index,
std::vector<float> *out_preds) {
int nthread;
#pragma omp parallel
{
nthread = omp_get_num_threads();
}
this->InitThreadTemp(nthread);
std::vector<float> &preds = *out_preds;
preds.resize(0);
// start collecting the prediction
utils::IIterator<SparseBatch> *iter = fmat.RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
const SparseBatch &batch = iter->Value();
utils::Assert(batch.base_rowid * mparam.num_output_group == preds.size(),
"base_rowid is not set correctly");
// output convention: nrow * k, where nrow is number of rows
// k is number of group
preds.resize(preds.size() + batch.size * mparam.num_output_group);
// parallel over local batch
const unsigned nsize = static_cast<unsigned>(batch.size);
#pragma omp parallel for schedule(static)
for (unsigned i = 0; i < nsize; ++i) {
const int tid = omp_get_thread_num();
std::vector<float> &feats = thread_temp[tid];
const size_t ridx = batch.base_rowid + i;
const unsigned root_idx = root_index.size() == 0 ? 0 : root_index[ridx];
// loop over output groups
for (int gid = 0; gid < mparam.num_output_group; ++gid) {
preds[ridx * mparam.num_output_group + gid] =
this->Pred(batch[i],
buffer_offset < 0 ? -1 : buffer_offset+ridx,
gid, root_idx, &feats);
}
}
}
}
protected:
// clear the model
inline void Clear(void) {
for (size_t i = 0; i < trees.size(); ++i) {
delete trees[i];
}
trees.clear();
pred_buffer.clear();
pred_counter.clear();
}
// initialize updater before using them
inline void InitUpdater(void) {
if (tparam.updater_initialized != 0) return;
for (size_t i = 0; i < updaters.size(); ++i) {
delete updaters[i];
}
updaters.clear();
std::string tval = tparam.updater_seq;
char *saveptr, *pstr;
pstr = strtok_r(&tval[0], ",", &saveptr);
while (pstr != NULL) {
updaters.push_back(tree::CreateUpdater<FMatrix>(pstr));
for (size_t j = 0; j < cfg.size(); ++j) {
// set parameters
updaters.back()->SetParam(cfg[j].first.c_str(), cfg[j].second.c_str());
}
pstr = strtok_r(NULL, ",", &saveptr);
}
tparam.updater_initialized = 1;
}
// do group specific group
inline void BoostNewTrees(const std::vector<bst_gpair> &gpair,
FMatrix &fmat,
const std::vector<unsigned> &root_index,
int bst_group) {
this->InitUpdater();
// create the trees
std::vector<tree::RegTree *> new_trees;
for (int i = 0; i < tparam.num_parallel_tree; ++i) {
new_trees.push_back(new tree::RegTree());
for (size_t j = 0; j < cfg.size(); ++j) {
new_trees.back()->param.SetParam(cfg[j].first.c_str(), cfg[j].second.c_str());
}
new_trees.back()->InitModel();
}
// update the trees
for (size_t i = 0; i < updaters.size(); ++i) {
updaters[i]->Update(gpair, fmat, root_index, new_trees);
}
// push back to model
for (size_t i = 0; i < new_trees.size(); ++i) {
trees.push_back(new_trees[i]);
tree_info.push_back(bst_group);
}
mparam.num_trees += tparam.num_parallel_tree;
}
// make a prediction for a single instance
inline float Pred(const SparseBatch::Inst &inst,
int64_t buffer_index,
int bst_group,
unsigned root_index,
std::vector<float> *p_feats) {
size_t itop = 0;
float psum = 0.0f;
const int bid = mparam.BufferOffset(buffer_index, bst_group);
// load buffered results if any
if (bid >= 0) {
itop = pred_counter[bid];
psum = pred_buffer[bid];
}
if (itop != trees.size()) {
FillThreadTemp(inst, p_feats);
for (size_t i = itop; i < trees.size(); ++i) {
if (tree_info[i] == bst_group) {
psum += trees[i]->Predict(*p_feats, root_index);
}
}
DropThreadTemp(inst, p_feats);
}
// updated the buffered results
if (bid >= 0) {
pred_counter[bid] = static_cast<unsigned>(trees.size());
pred_buffer[bid] = psum;
}
return psum;
}
// initialize thread local space for prediction
inline void InitThreadTemp(int nthread) {
thread_temp.resize(nthread);
for (size_t i = 0; i < thread_temp.size(); ++i) {
thread_temp[i].resize(mparam.num_feature);
std::fill(thread_temp[i].begin(), thread_temp[i].end(), NAN);
}
}
// fill in a thread local dense vector using a sparse instance
inline static void FillThreadTemp(const SparseBatch::Inst &inst,
std::vector<float> *p_feats) {
std::vector<float> &feats = *p_feats;
for (bst_uint i = 0; i < inst.length; ++i) {
feats[inst[i].findex] = inst[i].fvalue;
}
}
// clear up a thread local dense vector
inline static void DropThreadTemp(const SparseBatch::Inst &inst,
std::vector<float> *p_feats) {
std::vector<float> &feats = *p_feats;
for (bst_uint i = 0; i < inst.length; ++i) {
feats[inst[i].findex] = NAN;
}
}
// --- data structure ---
/*! \brief training parameters */
struct TrainParam {
/*! \brief number of threads */
int nthread;
/*!
* \brief number of parallel trees constructed each iteration
* use this option to support boosted random forest
*/
int num_parallel_tree;
/*! \brief whether updater is already initialized */
int updater_initialized;
/*! \brief tree updater sequence */
std::string updater_seq;
// construction
TrainParam(void) {
nthread = 0;
updater_seq = "grow_colmaker,prune";
num_parallel_tree = 1;
updater_initialized = 0;
}
inline void SetParam(const char *name, const char *val){
if (!strcmp(name, "updater") &&
strcmp(updater_seq.c_str(), val) != 0) {
updater_seq = val;
updater_initialized = 0;
}
if (!strcmp(name, "nthread")) {
omp_set_num_threads(nthread);
nthread = atoi(val);
}
if (!strcmp(name, "num_parallel_tree")) {
num_parallel_tree = atoi(val);
}
}
};
/*! \brief model parameters */
struct ModelParam {
/*! \brief number of trees */
int num_trees;
/*! \brief number of root: default 0, means single tree */
int num_roots;
/*! \brief number of features to be used by trees */
int num_feature;
/*! \brief size of predicton buffer allocated used for buffering */
int64_t num_pbuffer;
/*!
* \brief how many output group a single instance can produce
* this affects the behavior of number of output we have:
* suppose we have n instance and k group, output will be k*n
*/
int num_output_group;
/*! \brief reserved parameters */
int reserved[32];
/*! \brief constructor */
ModelParam(void) {
num_trees = 0;
num_roots = num_feature = 0;
num_pbuffer = 0;
num_output_group = 1;
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("num_pbuffer", name)) num_pbuffer = atol(val);
if (!strcmp("num_output_group", name)) num_output_group = atol(val);
if (!strcmp("bst:num_roots", name)) num_roots = atoi(val);
if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
}
/*! \return size of prediction buffer actually needed */
inline size_t PredBufferSize(void) const {
return num_output_group * num_pbuffer;
}
/*!
* \brief get the buffer offset given a buffer index and group id
* \return calculated buffer offset
*/
inline size_t BufferOffset(int64_t buffer_index, int bst_group) const {
if (buffer_index < 0) return -1;
utils::Check(buffer_index < num_pbuffer, "buffer_index exceed num_pbuffer");
return buffer_index + num_pbuffer * bst_group;
}
};
// training parameter
TrainParam tparam;
// model parameter
ModelParam mparam;
/*! \brief vector of trees stored in the model */
std::vector<tree::RegTree*> trees;
/*! \brief some information indicator of the tree, reserved */
std::vector<int> tree_info;
/*! \brief prediction buffer */
std::vector<float> pred_buffer;
/*! \brief prediction buffer counter, remember the prediction */
std::vector<unsigned> pred_counter;
// ----training fields----
// configurations for tree
std::vector< std::pair<std::string, std::string> > cfg;
// temporal storage for per thread
std::vector< std::vector<float> > thread_temp;
// the updaters that can be applied to each of tree
std::vector< tree::IUpdater<FMatrix>* > updaters;
};
} // namespace gbm
} // namespace xgboost
#endif // XGBOOST_GBM_GBTREE_INL_HPP_

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#ifndef XGBOOST_LEARNER_DMATRIX_H_
#define XGBOOST_LEARNER_DMATRIX_H_
/*!
* \file dmatrix.h
* \brief meta data and template data structure
* used for regression/classification/ranking
* \author Tianqi Chen
*/
#include "../data.h"
namespace xgboost {
namespace learner {
/*!
* \brief meta information needed in training, including label, weight
*/
struct MetaInfo {
/*! \brief label of each instance */
std::vector<float> labels;
/*!
* \brief the index of begin and end of a group
* needed when the learning task is ranking
*/
std::vector<bst_uint> group_ptr;
/*! \brief weights of each instance, optional */
std::vector<float> weights;
/*!
* \brief specified root index of each instance,
* can be used for multi task setting
*/
std::vector<unsigned> root_index;
/*! \brief get weight of each instances */
inline float GetWeight(size_t i) const {
if(weights.size() != 0) {
return weights[i];
} else {
return 1.0f;
}
}
/*! \brief get root index of i-th instance */
inline float GetRoot(size_t i) const {
if(root_index.size() != 0) {
return static_cast<float>(root_index[i]);
} else {
return 0;
}
}
inline void SaveBinary(utils::IStream &fo) {
fo.Write(labels);
fo.Write(group_ptr);
fo.Write(weights);
fo.Write(root_index);
}
inline void LoadBinary(utils::IStream &fi) {
utils::Check(fi.Read(&labels), "MetaInfo: invalid format");
utils::Check(fi.Read(&group_ptr), "MetaInfo: invalid format");
utils::Check(fi.Read(&weights), "MetaInfo: invalid format");
utils::Check(fi.Read(&root_index), "MetaInfo: invalid format");
}
};
/*!
* \brief data object used for learning,
* \tparam FMatrix type of feature data source
*/
template<typename FMatrix>
struct DMatrix {
/*! \brief meta information about the dataset */
MetaInfo info;
/*! \brief number of rows in the DMatrix */
size_t num_row;
/*! \brief feature matrix about data content */
FMatrix fmat;
/*!
* \brief cache pointer to verify if the data structure is cached in some learner
* used to verify if DMatrix is cached
*/
void *cache_learner_ptr_;
/*! \brief default constructor */
DMatrix(void) : cache_learner_ptr_(NULL) {}
};
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_DMATRIX_H_

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#ifndef XGBOOST_LEARNER_EVALUATION_INL_HPP_
#define XGBOOST_LEARNER_EVALUATION_INL_HPP_
/*!
* \file xgboost_evaluation-inl.hpp
* \brief evaluation metrics for regression and classification and rank
* \author Kailong Chen, Tianqi Chen
*/
#include <vector>
#include <utility>
#include <string>
#include <climits>
#include <algorithm>
#include "./evaluation.h"
#include "./helper_utils.h"
namespace xgboost {
namespace learner {
/*!
* \brief base class of elementwise evaluation
* \tparam Derived the name of subclass
*/
template<typename Derived>
struct EvalEWiseBase : public IEvaluator {
virtual float Eval(const std::vector<float> &preds,
const MetaInfo &info) const {
utils::Check(preds.size() == info.labels.size(),
"label and prediction size not match");
const unsigned ndata = static_cast<unsigned>(preds.size());
float sum = 0.0, wsum = 0.0;
#pragma omp parallel for reduction(+:sum, wsum) schedule(static)
for (unsigned i = 0; i < ndata; ++i) {
const float wt = info.GetWeight(i);
sum += Derived::EvalRow(info.labels[i], preds[i]) * wt;
wsum += wt;
}
return Derived::GetFinal(sum, wsum);
}
/*!
* \brief to be implemented by subclass,
* get evaluation result from one row
* \param label label of current instance
* \param pred prediction value of current instance
* \param weight weight of current instance
*/
inline static float EvalRow(float label, float pred);
/*!
* \brief to be overide by subclas, final trasnformation
* \param esum the sum statistics returned by EvalRow
* \param wsum sum of weight
*/
inline static float GetFinal(float esum, float wsum) {
return esum / wsum;
}
};
/*! \brief RMSE */
struct EvalRMSE : public EvalEWiseBase<EvalRMSE> {
virtual const char *Name(void) const {
return "rmse";
}
inline static float EvalRow(float label, float pred) {
float diff = label - pred;
return diff * diff;
}
inline static float GetFinal(float esum, float wsum) {
return std::sqrt(esum / wsum);
}
};
/*! \brief logloss */
struct EvalLogLoss : public EvalEWiseBase<EvalLogLoss> {
virtual const char *Name(void) const {
return "logloss";
}
inline static float EvalRow(float y, float py) {
return - y * std::log(py) - (1.0f - y) * std::log(1 - py);
}
};
/*! \brief error */
struct EvalError : public EvalEWiseBase<EvalError> {
virtual const char *Name(void) const {
return "error";
}
inline static float EvalRow(float label, float pred) {
// assume label is in [0,1]
return pred > 0.5f ? 1.0f - label : label;
}
};
/*! \brief match error */
struct EvalMatchError : public EvalEWiseBase<EvalMatchError> {
virtual const char *Name(void) const {
return "merror";
}
inline static float EvalRow(float label, float pred) {
return static_cast<int>(pred) != static_cast<int>(label);
}
};
/*! \brief AMS: also records best threshold */
struct EvalAMS : public IEvaluator {
public:
explicit EvalAMS(const char *name) {
name_ = name;
// note: ams@0 will automatically select which ratio to go
utils::Check(sscanf(name, "ams@%f", &ratio_) == 1, "invalid ams format");
}
virtual float Eval(const std::vector<float> &preds,
const MetaInfo &info) const {
const unsigned ndata = static_cast<unsigned>(preds.size());
utils::Check(info.weights.size() == ndata, "we need weight to evaluate ams");
std::vector< std::pair<float, unsigned> > rec(ndata);
#pragma omp parallel for schedule(static)
for (unsigned i = 0; i < ndata; ++i) {
rec[i] = std::make_pair(preds[i], i);
}
std::sort(rec.begin(), rec.end(), CmpFirst);
unsigned ntop = static_cast<unsigned>(ratio_ * ndata);
if (ntop == 0) ntop = ndata;
const double br = 10.0;
unsigned thresindex = 0;
double s_tp = 0.0, b_fp = 0.0, tams = 0.0;
for (unsigned i = 0; i < ndata-1 && i < ntop; ++i) {
const unsigned ridx = rec[i].second;
const float wt = info.weights[ridx];
if (info.labels[ridx] > 0.5f) {
s_tp += wt;
} else {
b_fp += wt;
}
if (rec[i].first != rec[i+1].first) {
double ams = sqrtf(2*((s_tp+b_fp+br) * log(1.0 + s_tp/(b_fp+br)) - s_tp));
if (tams < ams) {
thresindex = i;
tams = ams;
}
}
}
if (ntop == ndata) {
fprintf(stderr, "\tams-ratio=%g", static_cast<float>(thresindex) / ndata);
return tams;
} else {
return sqrtf(2*((s_tp+b_fp+br) * log(1.0 + s_tp/(b_fp+br)) - s_tp));
}
}
virtual const char *Name(void) const {
return name_.c_str();
}
private:
std::string name_;
float ratio_;
};
/*! \brief Area under curve, for both classification and rank */
struct EvalAuc : public IEvaluator {
virtual float Eval(const std::vector<float> &preds,
const MetaInfo &info) const {
utils::Check(preds.size() == info.labels.size(), "label size predict size not match");
std::vector<unsigned> tgptr(2, 0); tgptr[1] = preds.size();
const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
utils::Check(gptr.back() == preds.size(),
"EvalAuc: group structure must match number of prediction");
const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
// sum statictis
double sum_auc = 0.0f;
#pragma omp parallel reduction(+:sum_auc)
{
// each thread takes a local rec
std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k) {
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
rec.push_back(std::make_pair(preds[j], j));
}
std::sort(rec.begin(), rec.end(), CmpFirst);
// calculate AUC
double sum_pospair = 0.0;
double sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
for (size_t j = 0; j < rec.size(); ++j) {
const float wt = info.GetWeight(rec[j].second);
const float ctr = info.labels[rec[j].second];
// keep bucketing predictions in same bucket
if (j != 0 && rec[j].first != rec[j - 1].first) {
sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
sum_npos += buf_pos; sum_nneg += buf_neg;
buf_neg = buf_pos = 0.0f;
}
buf_pos += ctr * wt; buf_neg += (1.0f - ctr) * wt;
}
sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
sum_npos += buf_pos; sum_nneg += buf_neg;
// check weird conditions
utils::Check(sum_npos > 0.0 && sum_nneg > 0.0,
"AUC: the dataset only contains pos or neg samples");
// this is the AUC
sum_auc += sum_pospair / (sum_npos*sum_nneg);
}
}
// return average AUC over list
return static_cast<float>(sum_auc) / ngroup;
}
virtual const char *Name(void) const {
return "auc";
}
};
/*! \brief Evaluate rank list */
struct EvalRankList : public IEvaluator {
public:
virtual float Eval(const std::vector<float> &preds,
const MetaInfo &info) const {
utils::Check(preds.size() == info.labels.size(),
"label size predict size not match");
const std::vector<unsigned> &gptr = info.group_ptr;
utils::Assert(gptr.size() != 0, "must specify group when constructing rank file");
utils::Assert(gptr.back() == preds.size(),
"EvalRanklist: group structure must match number of prediction");
const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
// sum statistics
double sum_metric = 0.0f;
#pragma omp parallel reduction(+:sum_metric)
{
// each thread takes a local rec
std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k) {
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
rec.push_back(std::make_pair(preds[j], static_cast<int>(info.labels[j])));
}
sum_metric += this->EvalMetric(rec);
}
}
return static_cast<float>(sum_metric) / ngroup;
}
virtual const char *Name(void) const {
return name_.c_str();
}
protected:
explicit EvalRankList(const char *name) {
name_ = name;
minus_ = false;
if (sscanf(name, "%*[^@]@%u[-]?", &topn_) != 1) {
topn_ = UINT_MAX;
}
if (name[strlen(name) - 1] == '-') {
minus_ = true;
}
}
/*! \return evaluation metric, given the pair_sort record, (pred,label) */
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0;
protected:
unsigned topn_;
std::string name_;
bool minus_;
};
/*! \brief Precison at N, for both classification and rank */
struct EvalPrecision : public EvalRankList{
public:
explicit EvalPrecision(const char *name) : EvalRankList(name) {}
protected:
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const {
// calculate Preicsion
std::sort(rec.begin(), rec.end(), CmpFirst);
unsigned nhit = 0;
for (size_t j = 0; j < rec.size() && j < this->topn_; ++j) {
nhit += (rec[j].second != 0);
}
return static_cast<float>(nhit) / topn_;
}
};
/*! \brief NDCG */
struct EvalNDCG : public EvalRankList{
public:
explicit EvalNDCG(const char *name) : EvalRankList(name) {}
protected:
inline float CalcDCG(const std::vector< std::pair<float, unsigned> > &rec) const {
double sumdcg = 0.0;
for (size_t i = 0; i < rec.size() && i < this->topn_; ++i) {
const unsigned rel = rec[i].second;
if (rel != 0) {
sumdcg += ((1 << rel) - 1) / logf(i + 2);
}
}
return static_cast<float>(sumdcg);
}
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const {
std::stable_sort(rec.begin(), rec.end(), CmpFirst);
float dcg = this->CalcDCG(rec);
std::stable_sort(rec.begin(), rec.end(), CmpSecond);
float idcg = this->CalcDCG(rec);
if (idcg == 0.0f) {
if (minus_) {
return 0.0f;
} else {
return 1.0f;
}
}
return dcg/idcg;
}
};
/*! \brief Precison at N, for both classification and rank */
struct EvalMAP : public EvalRankList {
public:
explicit EvalMAP(const char *name) : EvalRankList(name) {}
protected:
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const {
std::sort(rec.begin(), rec.end(), CmpFirst);
unsigned nhits = 0;
double sumap = 0.0;
for (size_t i = 0; i < rec.size(); ++i) {
if (rec[i].second != 0) {
nhits += 1;
if (i < this->topn_) {
sumap += static_cast<float>(nhits) / (i+1);
}
}
}
if (nhits != 0) {
sumap /= nhits;
return static_cast<float>(sumap);
} else {
if (minus_) {
return 0.0f;
} else {
return 1.0f;
}
}
}
};
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_EVALUATION_INL_HPP_

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#ifndef XGBOOST_LEARNER_EVALUATION_H_
#define XGBOOST_LEARNER_EVALUATION_H_
/*!
* \file evaluation.h
* \brief interface of evaluation function supported in xgboost
* \author Tianqi Chen, Kailong Chen
*/
#include <string>
#include <vector>
#include "../utils/utils.h"
namespace xgboost {
namespace learner {
/*! \brief evaluator that evaluates the loss metrics */
struct IEvaluator{
/*!
* \brief evaluate a specific metric
* \param preds prediction
* \param info information, including label etc.
*/
virtual float Eval(const std::vector<float> &preds,
const MetaInfo &info) const = 0;
/*! \return name of metric */
virtual const char *Name(void) const = 0;
/*! \brief virtual destructor */
virtual ~IEvaluator(void) {}
};
} // namespace learner
} // namespace xgboost
// include implementations of evaluation functions
#include "evaluation-inl.hpp"
// factory function
namespace xgboost {
namespace learner {
inline IEvaluator* CreateEvaluator(const char *name) {
if (!strcmp(name, "rmse")) return new EvalRMSE();
if (!strcmp(name, "error")) return new EvalError();
if (!strcmp(name, "merror")) return new EvalMatchError();
if (!strcmp(name, "logloss")) return new EvalLogLoss();
if (!strcmp(name, "auc")) return new EvalAuc();
if (!strncmp(name, "ams@",4)) return new EvalAMS(name);
if (!strncmp(name, "pre@", 4)) return new EvalPrecision(name);
if (!strncmp(name, "map", 3)) return new EvalMAP(name);
if (!strncmp(name, "ndcg", 3)) return new EvalNDCG(name);
utils::Error("unknown evaluation metric type: %s", name);
return NULL;
}
/*! \brief a set of evaluators */
class EvalSet{
public:
inline void AddEval(const char *name) {
for (size_t i = 0; i < evals_.size(); ++i) {
if (!strcmp(name, evals_[i]->Name())) return;
}
evals_.push_back(CreateEvaluator(name));
}
~EvalSet(void) {
for (size_t i = 0; i < evals_.size(); ++i) {
delete evals_[i];
}
}
inline std::string Eval(const char *evname,
const std::vector<float> &preds,
const MetaInfo &info) const {
std::string result = "";
for (size_t i = 0; i < evals_.size(); ++i) {
float res = evals_[i]->Eval(preds, info);
char tmp[1024];
snprintf(tmp, sizeof(tmp), "\t%s-%s:%f", evname, evals_[i]->Name(), res);
result += tmp;
}
return result;
}
private:
std::vector<const IEvaluator*> evals_;
};
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_EVALUATION_H_

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#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
#define XGBOOST_LEARNER_HELPER_UTILS_H_
/*!
* \file helper_utils.h
* \brief useful helper functions
* \author Tianqi Chen, Kailong Chen
*/
#include <utility>
#include <vector>
#include <algorithm>
namespace xgboost {
namespace learner {
// simple helper function to do softmax
inline static void Softmax(std::vector<float>* p_rec) {
std::vector<float> &rec = *p_rec;
float wmax = rec[0];
for (size_t i = 1; i < rec.size(); ++i) {
wmax = std::max(rec[i], wmax);
}
double wsum = 0.0f;
for (size_t i = 0; i < rec.size(); ++i) {
rec[i] = std::exp(rec[i]-wmax);
wsum += rec[i];
}
for (size_t i = 0; i < rec.size(); ++i) {
rec[i] /= static_cast<float>(wsum);
}
}
// simple helper function to do softmax
inline static int FindMaxIndex(const std::vector<float>& rec) {
size_t mxid = 0;
for (size_t i = 1; i < rec.size(); ++i) {
if (rec[i] > rec[mxid] + 1e-6f) {
mxid = i;
}
}
return static_cast<int>(mxid);
}
inline static bool CmpFirst(const std::pair<float, unsigned> &a,
const std::pair<float, unsigned> &b) {
return a.first > b.first;
}
inline static bool CmpSecond(const std::pair<float, unsigned> &a,
const std::pair<float, unsigned> &b) {
return a.second > b.second;
}
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_HELPER_UTILS_H_

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#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
#define XGBOOST_LEARNER_LEARNER_INL_HPP_
/*!
* \file learner-inl.hpp
* \brief learning algorithm
* \author Tianqi Chen
*/
#include <algorithm>
#include <vector>
#include <utility>
#include <string>
#include "./objective.h"
#include "./evaluation.h"
#include "../gbm/gbm.h"
namespace xgboost {
/*! \brief namespace for learning algorithm */
namespace learner {
/*!
* \brief learner that takes do gradient boosting on specific objective functions
* and do training and prediction
*/
template<typename FMatrix>
class BoostLearner {
public:
BoostLearner(void) {
obj_ = NULL;
gbm_ = NULL;
name_obj_ = "reg:linear";
name_gbm_ = "gbtree";
}
~BoostLearner(void) {
if (obj_ != NULL) delete obj_;
if (gbm_ != NULL) delete gbm_;
}
/*!
* \brief add internal cache space for mat, this can speedup prediction for matrix,
* please cache prediction for training and eval data
* warning: if the model is loaded from file from some previous training history
* set cache data must be called with exactly SAME
* data matrices to continue training otherwise it will cause error
* \param mats array of pointers to matrix whose prediction result need to be cached
*/
inline void SetCacheData(const std::vector<DMatrix<FMatrix>*>& mats) {
// estimate feature bound
unsigned num_feature = 0;
// assign buffer index
size_t buffer_size = 0;
utils::Assert(cache_.size() == 0, "can only call cache data once");
for (size_t i = 0; i < mats.size(); ++i) {
bool dupilicate = false;
for (size_t j = 0; j < i; ++j) {
if (mats[i] == mats[j]) dupilicate = true;
}
if (dupilicate) continue;
// set mats[i]'s cache learner pointer to this
mats[i]->cache_learner_ptr_ = this;
cache_.push_back(CacheEntry(mats[i], buffer_size, mats[i]->num_row));
buffer_size += mats[i]->num_row;
num_feature = std::max(num_feature, static_cast<unsigned>(mats[i]->num_col));
}
char str_temp[25];
if (num_feature > mparam.num_feature) {
snprintf(str_temp, sizeof(str_temp), "%u", num_feature);
this->SetParam("bst:num_feature", str_temp);
}
snprintf(str_temp, sizeof(str_temp), "%lu", buffer_size);
this->SetParam("num_pbuffer", str_temp);
if (!silent) {
printf("buffer_size=%ld\n", buffer_size);
}
}
/*!
* \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(name, "silent")) silent = atoi(val);
if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
if (gbm_ == NULL) {
if (!strcmp(name, "objective")) name_obj_ = val;
if (!strcmp(name, "booster")) name_gbm_ = val;
mparam.SetParam(name, val);
}
cfg_.push_back(std::make_pair(std::string(name), std::string(val)));
}
/*!
* \brief initialize the model
*/
inline void InitModel(void) {
this->InitObjGBM();
// adapt the base score
mparam.base_score = obj_->ProbToMargin(mparam.base_score);
gbm_->InitModel();
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void LoadModel(utils::IStream &fi) {
utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
"BoostLearner: wrong model format");
utils::Check(fi.Read(&name_obj_), "BoostLearner: wrong model format");
utils::Check(fi.Read(&name_gbm_), "BoostLearner: wrong model format");
// delete existing gbm if any
if (obj_ != NULL) delete obj_;
if (gbm_ != NULL) delete gbm_;
this->InitObjGBM();
gbm_->LoadModel(fi);
}
/*!
* \brief load model from file
* \param fname file name
*/
inline void LoadModel(const char *fname) {
utils::FileStream fi(utils::FopenCheck(fname, "rb"));
this->LoadModel(fi);
fi.Close();
}
inline void SaveModel(utils::IStream &fo) const {
fo.Write(&mparam, sizeof(ModelParam));
fo.Write(&name_obj_);
fo.Write(&name_gbm_);
gbm_->SaveModel(fo);
}
/*!
* \brief save model into file
* \param fname file name
*/
inline void SaveModel(const char *fname) const {
utils::FileStream fo(utils::FopenCheck(fname, "wb"));
this->SaveModel(fo);
fo.Close();
}
/*!
* \brief update the model for one iteration
* \param iter current iteration number
* \param p_train pointer to the data matrix
*/
inline void UpdateOneIter(int iter, DMatrix<FMatrix> *p_train) {
this->PredictRaw(preds_, *p_train);
obj_->GetGradient(preds_, p_train->info, iter, &gpair_);
gbm_->DoBoost(gpair_, p_train->fmat, p_train->info.root_index);
}
/*!
* \brief evaluate the model for specific iteration
* \param iter iteration number
* \param evals datas i want to evaluate
* \param evname name of each dataset
* \return a string corresponding to the evaluation result
*/
inline std::string EvalOneIter(int iter,
const std::vector<const DMatrix<FMatrix>*> &evals,
const std::vector<std::string> &evname) {
std::string res;
char tmp[256];
snprintf(tmp, sizeof(tmp), "[%d]", iter);
res = tmp;
for (size_t i = 0; i < evals.size(); ++i) {
this->PredictRaw(*evals[i], &preds_);
obj_->EvalTransform(&preds_);
res += evaluator_.Eval(evname[i].c_str(), preds_, evals[i]->info);
}
return res;
}
/*!
* \brief simple evaluation function, with a specified metric
* \param data input data
* \param metric name of metric
* \return a pair of <evaluation name, result>
*/
std::pair<std::string, float> Evaluate(const DMatrix<FMatrix> &data, std::string metric) {
if (metric == "auto") metric = obj_->DefaultEvalMetric();
IEvaluator *ev = CreateEvaluator(metric.c_str());
this->PredictRaw(data, &preds_);
obj_->EvalTransform(&preds_);
float res = ev->Eval(preds_, data.info);
delete ev;
return std::make_pair(metric, res);
}
/*!
* \brief get prediction
* \param data input data
* \param out_preds output vector that stores the prediction
*/
inline void Predict(const DMatrix<FMatrix> &data,
std::vector<float> *out_preds) const {
this->PredictRaw(data, out_preds);
obj_->PredTransform(out_preds);
}
protected:
/*!
* \brief initialize the objective function and GBM,
* if not yet done
*/
inline void InitObjGBM(void) {
if (obj_ != NULL) return;
utils::Assert(gbm_ == NULL, "GBM and obj should be NULL");
obj_ = CreateObjFunction(name_obj_.c_str());
gbm_ = gbm::CreateGradBooster<FMatrix>(name_gbm_.c_str());
for (size_t i = 0; i < cfg_.size(); ++i) {
obj_->SetParam(cfg_[i].first.c_str(), cfg_[i].second.c_str());
gbm_->SetParam(cfg_[i].first.c_str(), cfg_[i].second.c_str());
}
evaluator_.AddEval(obj_->DefaultEvalMetric());
}
/*!
* \brief get un-transformed prediction
* \param data training data matrix
* \param out_preds output vector that stores the prediction
*/
inline void PredictRaw(const DMatrix<FMatrix> &data,
std::vector<float> *out_preds) {
gbm_->Predict(data.fmat, this->FindBufferOffset(data),
data.info, out_preds);
}
/*! \brief training parameter for regression */
struct ModelParam{
/* \brief global bias */
float base_score;
/* \brief number of features */
unsigned num_feature;
/* \brief number of class, if it is multi-class classification */
int num_class;
/*! \brief reserved field */
int reserved[32];
/*! \brief constructor */
ModelParam(void) {
base_score = 0.5f;
num_feature = 0;
num_class = 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("base_score", name)) base_score = static_cast<float>(atof(val));
if (!strcmp("num_class", name)) num_class = atoi(val);
if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
}
};
// data fields
// silent during training
int silent;
// evaluation set
EvalSet evaluator_;
// model parameter
ModelParam mparam;
// gbm model that back everything
gbm::IGradBooster<FMatrix> *gbm_;
// name of gbm model used for training
std::string name_gbm_;
// objective fnction
IObjFunction *obj_;
// name of objective function
std::string name_obj_;
// configurations
std::vector< std::pair<std::string, std::string> > cfg_;
// temporal storages for prediciton
std::vector<float> preds_;
// gradient pairs
std::vector<bst_gpair> gpair_;
private:
// cache entry object that helps handle feature caching
struct CacheEntry {
const DMatrix<FMatrix> *mat_;
size_t buffer_offset_;
size_t num_row_;
CacheEntry(const DMatrix<FMatrix> *mat, size_t buffer_offset, size_t num_row)
:mat_(mat), buffer_offset_(buffer_offset), num_row_(num_row) {}
};
// find internal bufer offset for certain matrix, if not exist, return -1
inline int64_t FindBufferOffset(const DMatrix<FMatrix> &mat) const {
for (size_t i = 0; i < cache_.size(); ++i) {
if (cache_[i].mat_ == &mat && mat.cache_learner_ptr_ == this) {
if (cache_[i].num_row_ == mat.num_row) {
return cache_[i].buffer_offset_;
}
}
}
return -1;
}
// data structure field
/*! \brief the entries indicates that we have internal prediction cache */
std::vector<CacheEntry> cache_;
};
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_LEARNER_INL_HPP_

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#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
/*!
* \file objective-inl.hpp
* \brief objective function implementations
* \author Tianqi Chen, Kailong Chen
*/
#include <vector>
#include "./objective.h"
namespace xgboost {
namespace learner {
/*! \brief defines functions to calculate some commonly used functions */
struct LossType {
/*! \brief indicate which type we are using */
int loss_type;
// list of constants
static const int kLinearSquare = 0;
static const int kLogisticNeglik = 1;
static const int kLogisticClassify = 2;
static const int kLogisticRaw = 3;
/*!
* \brief transform the linear sum to prediction
* \param x linear sum of boosting ensemble
* \return transformed prediction
*/
inline float PredTransform(float x) const {
switch (loss_type) {
case kLogisticRaw:
case kLinearSquare: return x;
case kLogisticClassify:
case kLogisticNeglik: return 1.0f / (1.0f + expf(-x));
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
/*!
* \brief calculate first order gradient of loss, given transformed prediction
* \param predt transformed prediction
* \param label true label
* \return first order gradient
*/
inline float FirstOrderGradient(float predt, float label) const {
switch (loss_type) {
case kLinearSquare: return predt - label;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt - label;
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
/*!
* \brief calculate second order gradient of loss, given transformed prediction
* \param predt transformed prediction
* \param label true label
* \return second order gradient
*/
inline float SecondOrderGradient(float predt, float label) const {
switch (loss_type) {
case kLinearSquare: return 1.0f;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt * (1 - predt);
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
/*!
* \brief transform probability value back to margin
*/
inline float ProbToMargin(float base_score) const {
if (loss_type == kLogisticRaw ||
loss_type == kLogisticClassify ||
loss_type == kLogisticNeglik ) {
utils::Check(base_score > 0.0f && base_score < 1.0f,
"base_score must be in (0,1) for logistic loss");
base_score = -logf(1.0f / base_score - 1.0f);
}
return base_score;
}
/*! \brief get default evaluation metric for the objective */
inline const char *DefaultEvalMetric(void) const {
if (loss_type == kLogisticClassify) return "error";
if (loss_type == kLogisticRaw) return "auc";
return "rmse";
}
};
/*! \brief objective function that only need to */
class RegLossObj : public IObjFunction{
public:
explicit RegLossObj(int loss_type) {
loss.loss_type = loss_type;
scale_pos_weight = 1.0f;
}
virtual ~RegLossObj(void) {}
virtual void SetParam(const char *name, const char *val) {
if (!strcmp("scale_pos_weight", name)) {
scale_pos_weight = static_cast<float>(atof(val));
}
}
virtual void GetGradient(const std::vector<float>& preds,
const MetaInfo &info,
int iter,
std::vector<bst_gpair> *out_gpair) {
utils::Check(preds.size() == info.labels.size(),
"labels are not correctly provided");
std::vector<bst_gpair> &gpair = *out_gpair;
gpair.resize(preds.size());
// start calculating gradient
const unsigned ndata = static_cast<unsigned>(preds.size());
#pragma omp parallel for schedule(static)
for (unsigned j = 0; j < ndata; ++j) {
float p = loss.PredTransform(preds[j]);
float w = info.GetWeight(j);
if (info.labels[j] == 1.0f) w *= scale_pos_weight;
gpair[j] = bst_gpair(loss.FirstOrderGradient(p, info.labels[j]) * w,
loss.SecondOrderGradient(p, info.labels[j]) * w);
}
}
virtual const char* DefaultEvalMetric(void) {
return loss.DefaultEvalMetric();
}
virtual void PredTransform(std::vector<float> *io_preds) {
std::vector<float> &preds = *io_preds;
const unsigned ndata = static_cast<unsigned>(preds.size());
#pragma omp parallel for schedule(static)
for (unsigned j = 0; j < ndata; ++j) {
preds[j] = loss.PredTransform(preds[j]);
}
}
protected:
float scale_pos_weight;
LossType loss;
};
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_OBJECTIVE_INL_HPP_

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#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
#define XGBOOST_LEARNER_OBJECTIVE_H_
/*!
* \file objective.h
* \brief interface of objective function used for gradient boosting
* \author Tianqi Chen, Kailong Chen
*/
#include "dmatrix.h"
namespace xgboost {
namespace learner {
/*! \brief interface of objective function */
class IObjFunction{
public:
/*! \brief virtual destructor */
virtual ~IObjFunction(void){}
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
virtual void SetParam(const char *name, const char *val) = 0;
/*!
* \brief get gradient over each of predictions, given existing information
* \param preds prediction of current round
* \param info information about labels, weights, groups in rank
* \param iter current iteration number
* \param out_gpair output of get gradient, saves gradient and second order gradient in
*/
virtual void GetGradient(const std::vector<float>& preds,
const MetaInfo &info,
int iter,
std::vector<bst_gpair> *out_gpair) = 0;
/*! \return the default evaluation metric for the objective */
virtual const char* DefaultEvalMetric(void) = 0;
// the following functions are optional, most of time default implementation is good enough
/*!
* \brief transform prediction values, this is only called when Prediction is called
* \param io_preds prediction values, saves to this vector as well
*/
virtual void PredTransform(std::vector<float> *io_preds){}
/*!
* \brief transform prediction values, this is only called when Eval is called,
* usually it redirect to PredTransform
* \param io_preds prediction values, saves to this vector as well
*/
virtual void EvalTransform(std::vector<float> *io_preds) {
this->PredTransform(io_preds);
}
/*!
* \brief transform probability value back to margin
* this is used to transform user-set base_score back to margin
* used by gradient boosting
* \return transformed value
*/
virtual float ProbToMargin(float base_score) {
return base_score;
}
};
} // namespace learner
} // namespace xgboost
// this are implementations of objective functions
#include "objective-inl.hpp"
// factory function
namespace xgboost {
namespace learner {
/*! \brief factory funciton to create objective function by name */
inline IObjFunction* CreateObjFunction(const char *name) {
if (!strcmp("reg:linear", name)) return new RegLossObj( LossType::kLinearSquare );
if (!strcmp("reg:logistic", name)) return new RegLossObj( LossType::kLogisticNeglik );
if (!strcmp("binary:logistic", name)) return new RegLossObj( LossType::kLogisticClassify );
if (!strcmp("binary:logitraw", name)) return new RegLossObj( LossType::kLogisticRaw );
utils::Error("unknown objective function type: %s", name);
return NULL;
}
} // namespace learner
} // namespace xgboost
#endif // XGBOOST_LEARNER_OBJECTIVE_H_

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#ifndef XGBOOST_REGRANK_H
#define XGBOOST_REGRANK_H
/*!
* \file xgboost_regrank.h
* \brief class for gradient boosted regression and ranking
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <cmath>
#include <cstdlib>
#include <cstring>
#include "xgboost_regrank_data.h"
#include "xgboost_regrank_eval.h"
#include "xgboost_regrank_obj.h"
#include "../utils/xgboost_omp.h"
#include "../booster/xgboost_gbmbase.h"
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_stream.h"
namespace xgboost{
namespace regrank{
/*! \brief class for gradient boosted regression and ranking */
class RegRankBoostLearner{
public:
/*! \brief constructor */
RegRankBoostLearner(void){
silent = 0;
obj_ = NULL;
name_obj_ = "reg:linear";
}
/*! \brief destructor */
~RegRankBoostLearner(void){
if( obj_ != NULL ) delete obj_;
}
/*!
* \brief a regression booter associated with training and evaluating data
* \param mats array of pointers to matrix whose prediction result need to be cached
*/
RegRankBoostLearner(const std::vector<DMatrix *>& mats){
silent = 0;
obj_ = NULL;
name_obj_ = "reg:linear";
this->SetCacheData(mats);
}
/*!
* \brief add internal cache space for mat, this can speedup prediction for matrix,
* please cache prediction for training and eval data
* warning: if the model is loaded from file from some previous training history
* set cache data must be called with exactly SAME
* data matrices to continue training otherwise it will cause error
* \param mats array of pointers to matrix whose prediction result need to be cached
*/
inline void SetCacheData(const std::vector<DMatrix *>& mats){
// estimate feature bound
int num_feature = 0;
// assign buffer index
unsigned buffer_size = 0;
utils::Assert( cache_.size() == 0, "can only call cache data once" );
for( size_t i = 0; i < mats.size(); ++i ){
bool dupilicate = false;
for( size_t j = 0; j < i; ++ j ){
if( mats[i] == mats[j] ) dupilicate = true;
}
if( dupilicate ) continue;
// set mats[i]'s cache learner pointer to this
mats[i]->cache_learner_ptr_ = this;
cache_.push_back( CacheEntry( mats[i], buffer_size, mats[i]->Size() ) );
buffer_size += static_cast<unsigned>(mats[i]->Size());
num_feature = std::max(num_feature, (int)(mats[i]->data.NumCol()));
}
char str_temp[25];
if (num_feature > mparam.num_feature){
mparam.num_feature = num_feature;
sprintf(str_temp, "%d", num_feature);
base_gbm.SetParam("bst:num_feature", str_temp);
}
sprintf(str_temp, "%u", buffer_size);
base_gbm.SetParam("num_pbuffer", str_temp);
if (!silent){
printf("buffer_size=%u\n", buffer_size);
}
}
/*!
* \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(name, "silent")) silent = atoi(val);
if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
if (!strcmp(name, "objective") ) name_obj_ = val;
if (!strcmp(name, "num_class") ) base_gbm.SetParam("num_booster_group", val );
mparam.SetParam(name, val);
base_gbm.SetParam(name, val);
cfg_.push_back( std::make_pair( std::string(name), std::string(val) ) );
}
/*!
* \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( mparam.num_class != 0 ){
if( name_obj_ != "multi:softmax" && name_obj_ != "multi:softprob"){
name_obj_ = "multi:softmax";
printf("auto select objective=softmax to support multi-class classification\n" );
}
}
base_gbm.InitTrainer();
obj_ = CreateObjFunction( name_obj_.c_str() );
for( size_t i = 0; i < cfg_.size(); ++ i ){
obj_->SetParam( cfg_[i].first.c_str(), cfg_[i].second.c_str() );
}
evaluator_.AddEval( obj_->DefaultEvalMetric() );
}
/*!
* \brief initialize the current data storage for model, if the model is used first time, call this function
*/
inline void InitModel(void){
base_gbm.InitModel();
mparam.AdjustBase(name_obj_.c_str());
}
/*!
* \brief load model from file
* \param fname file name
*/
inline void LoadModel(const char *fname){
utils::FileStream fi(utils::FopenCheck(fname, "rb"));
this->LoadModel(fi);
fi.Close();
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void LoadModel(utils::IStream &fi){
base_gbm.LoadModel(fi);
utils::Assert(fi.Read(&mparam, sizeof(ModelParam)) != 0);
// save name obj
size_t len;
if( fi.Read(&len, sizeof(len)) != 0 ){
name_obj_.resize( len );
if( len != 0 ){
utils::Assert( fi.Read(&name_obj_[0], len*sizeof(char)) != 0 );
}
}
}
/*!
* \brief DumpModel
* \param fo text file
* \param fmap feature map that may help give interpretations of feature
* \param with_stats whether print statistics as well
*/
inline void DumpModel(FILE *fo, const utils::FeatMap& fmap, bool with_stats){
base_gbm.DumpModel(fo, fmap, with_stats);
}
/*!
* \brief Dump path of all trees
* \param fo text file
* \param data input data
*/
inline void DumpPath(FILE *fo, const DMatrix &data){
base_gbm.DumpPath(fo, data.data);
}
/*!
* \brief save model to stream
* \param fo output stream
*/
inline void SaveModel(utils::IStream &fo) const{
base_gbm.SaveModel(fo);
fo.Write(&mparam, sizeof(ModelParam));
// save name obj
size_t len = name_obj_.length();
fo.Write(&len, sizeof(len));
fo.Write(&name_obj_[0], len*sizeof(char));
}
/*!
* \brief save model into file
* \param fname file name
*/
inline void SaveModel(const char *fname) const{
utils::FileStream fo(utils::FopenCheck(fname, "wb"));
this->SaveModel(fo);
fo.Close();
}
/*!
* \brief update the model for one iteration
*/
inline void UpdateOneIter(const DMatrix &train){
this->PredictRaw(preds_, train);
obj_->GetGradient(preds_, train.info, base_gbm.NumBoosters(), grad_, hess_);
if( grad_.size() == train.Size() ){
base_gbm.DoBoost(grad_, hess_, train.data, train.info.root_index);
}else{
int ngroup = base_gbm.NumBoosterGroup();
utils::Assert( grad_.size() == train.Size() * (size_t)ngroup, "BUG: UpdateOneIter: mclass" );
std::vector<float> tgrad( train.Size() ), thess( train.Size() );
for( int g = 0; g < ngroup; ++ g ){
memcpy( &tgrad[0], &grad_[g*tgrad.size()], sizeof(float)*tgrad.size() );
memcpy( &thess[0], &hess_[g*tgrad.size()], sizeof(float)*tgrad.size() );
base_gbm.DoBoost(tgrad, thess, train.data, train.info.root_index, g );
}
}
}
/*!
* \brief evaluate the model for specific iteration
* \param iter iteration number
* \param evals datas i want to evaluate
* \param evname name of each dataset
* \param fo file to output log
*/
inline void EvalOneIter(int iter,
const std::vector<const DMatrix*> &evals,
const std::vector<std::string> &evname,
FILE *fo=stderr ){
fprintf(fo, "[%d]", iter);
for (size_t i = 0; i < evals.size(); ++i){
this->PredictRaw(preds_, *evals[i]);
obj_->EvalTransform(preds_);
evaluator_.Eval(fo, evname[i].c_str(), preds_, evals[i]->info);
}
fprintf(fo, "\n");
fflush(fo);
}
/*!
* \brief get prediction
* \param storage to store prediction
* \param data input data
* \param bst_group booster group we are in
*/
inline void Predict(std::vector<float> &preds, const DMatrix &data, int bst_group = -1){
this->PredictRaw( preds, data, bst_group );
obj_->PredTransform( preds );
}
public:
/*!
* \brief interactive update
* \param action action type
* \parma train training data
*/
inline void UpdateInteract(std::string action, const DMatrix& train){
for(size_t i = 0; i < cache_.size(); ++i){
this->InteractPredict(preds_, *cache_[i].mat_);
}
if (action == "remove"){
base_gbm.DelteBooster(); return;
}
obj_->GetGradient(preds_, train.info, base_gbm.NumBoosters(), grad_, hess_);
std::vector<unsigned> root_index;
base_gbm.DoBoost(grad_, hess_, train.data, root_index);
for(size_t i = 0; i < cache_.size(); ++i){
this->InteractRePredict(*cache_[i].mat_);
}
}
private:
/*! \brief get the transformed predictions, given data */
inline void InteractPredict(std::vector<float> &preds, const DMatrix &data){
int buffer_offset = this->FindBufferOffset(data);
utils::Assert( buffer_offset >=0, "interact mode must cache training data" );
preds.resize(data.Size());
const unsigned ndata = static_cast<unsigned>(data.Size());
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
preds[j] = mparam.base_score + base_gbm.InteractPredict(data.data, j, buffer_offset + j);
}
obj_->PredTransform( preds );
}
/*! \brief repredict trial */
inline void InteractRePredict(const DMatrix &data){
int buffer_offset = this->FindBufferOffset(data);
utils::Assert( buffer_offset >=0, "interact mode must cache training data" );
const unsigned ndata = static_cast<unsigned>(data.Size());
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
base_gbm.InteractRePredict(data.data, j, buffer_offset + j);
}
}
/*! \brief get un-transformed prediction*/
inline void PredictRaw(std::vector<float> &preds, const DMatrix &data, int bst_group = -1 ){
int buffer_offset = this->FindBufferOffset(data);
if( bst_group < 0 ){
int ngroup = base_gbm.NumBoosterGroup();
preds.resize( data.Size() * ngroup );
for( int g = 0; g < ngroup; ++ g ){
this->PredictBuffer(&preds[ data.Size() * g ], data, buffer_offset, g );
}
}else{
preds.resize( data.Size() );
this->PredictBuffer(&preds[0], data, buffer_offset, bst_group );
}
}
/*! \brief get the un-transformed predictions, given data */
inline void PredictBuffer(float *preds, const DMatrix &data, int buffer_offset, int bst_group ){
const unsigned ndata = static_cast<unsigned>(data.Size());
if( buffer_offset >= 0 ){
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
preds[j] = mparam.base_score + base_gbm.Predict(data.data, j, buffer_offset + j, data.info.GetRoot(j), bst_group );
}
}else
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
preds[j] = mparam.base_score + base_gbm.Predict(data.data, j, -1, data.info.GetRoot(j), bst_group );
}{
}
}
private:
/*! \brief training parameter for regression */
struct ModelParam{
/* \brief global bias */
float base_score;
/* \brief type of loss function */
int loss_type;
/* \brief number of features */
int num_feature;
/* \brief number of class, if it is multi-class classification */
int num_class;
/*! \brief reserved field */
int reserved[15];
/*! \brief constructor */
ModelParam(void){
base_score = 0.5f;
loss_type = -1;
num_feature = 0;
num_class = 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("base_score", name)) base_score = (float)atof(val);
if (!strcmp("num_class", name)) num_class = atoi(val);
if (!strcmp("loss_type", name)) loss_type = atoi(val);
if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
}
/*!
* \brief adjust base_score based on loss type and objective function
*/
inline void AdjustBase(const char *obj){
// some tweaks for loss type
if( loss_type == -1 ){
loss_type = 1;
if( !strcmp("reg:linear", obj ) ) loss_type = 0;
}
if (loss_type == 1 || loss_type == 2|| loss_type == 3){
utils::Assert(base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain");
base_score = -logf(1.0f / base_score - 1.0f);
}
}
};
private:
struct CacheEntry{
const DMatrix *mat_;
int buffer_offset_;
size_t num_row_;
CacheEntry(const DMatrix *mat, int buffer_offset, size_t num_row)
:mat_(mat), buffer_offset_(buffer_offset), num_row_(num_row){}
};
/*! \brief the entries indicates that we have internal prediction cache */
std::vector<CacheEntry> cache_;
private:
// find internal bufer offset for certain matrix, if not exist, return -1
inline int FindBufferOffset(const DMatrix &mat){
for(size_t i = 0; i < cache_.size(); ++i){
if( cache_[i].mat_ == &mat && mat.cache_learner_ptr_ == this ) {
if( cache_[i].num_row_ == mat.Size() ){
return cache_[i].buffer_offset_;
}else{
fprintf( stderr, "warning: number of rows in input matrix changed as remembered in cachelist, ignore cached results\n" );
fflush( stderr );
}
}
}
return -1;
}
protected:
int silent;
EvalSet evaluator_;
booster::GBMBase base_gbm;
ModelParam mparam;
// objective fnction
IObjFunction *obj_;
// name of objective function
std::string name_obj_;
std::vector< std::pair<std::string, std::string> > cfg_;
protected:
std::vector<float> grad_, hess_, preds_;
};
}
};
#endif

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#ifndef XGBOOST_REGRANK_DATA_H
#define XGBOOST_REGRANK_DATA_H
/*!
* \file xgboost_regrank_data.h
* \brief input data structure for regression, binary classification, and rankning.
* Format:
* The data should contain each data instance in each line.
* The format of line data is as below:
* label <nonzero feature dimension> [feature index:feature value]+
* When using rank, an addtional group file with suffix group must be provided, giving the number of instances in each group
* When using weighted aware classification(regression), an addtional weight file must be provided, giving the weight of each instance
*
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <cstdio>
#include <vector>
#include <string>
#include <cstring>
#include "../booster/xgboost_data.h"
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_stream.h"
namespace xgboost{
/*! \brief namespace to handle regression and rank */
namespace regrank{
/*! \brief data matrix for regression content */
struct DMatrix{
public:
/*! \brief data information besides the features */
struct Info{
/*! \brief label of each instance */
std::vector<float> labels;
/*! \brief the index of begin and end of a groupneeded when the learning task is ranking */
std::vector<unsigned> group_ptr;
/*! \brief weights of each instance, optional */
std::vector<float> weights;
/*! \brief specified root index of each instance, can be used for multi task setting*/
std::vector<unsigned> root_index;
/*! \brief get weight of each instances */
inline float GetWeight( size_t i ) const{
if( weights.size() != 0 ) return weights[i];
else return 1.0f;
}
inline float GetRoot( size_t i ) const{
if( root_index.size() != 0 ) return static_cast<float>(root_index[i]);
else return 0;
}
};
public:
/*! \brief feature data content */
booster::FMatrixS data;
/*! \brief information fields */
Info info;
/*!
* \brief cache pointer to verify if the data structure is cached in some learner
* this is a bit ugly, we need to have double check verification, so if one side get deleted,
* and some strange re-allocation gets the same pointer we will still be fine
*/
void *cache_learner_ptr_;
public:
/*! \brief default constructor */
DMatrix(void):cache_learner_ptr_(NULL){}
/*! \brief get the number of instances */
inline size_t Size() const{
return data.NumRow();
}
/*!
* \brief load from text file
* \param fname name of text data
* \param silent whether print information or not
*/
inline void LoadText(const char* fname, bool silent = false){
data.Clear();
FILE* file = utils::FopenCheck(fname, "r");
float label; bool init = true;
char tmp[1024];
std::vector<booster::bst_uint> findex;
std::vector<booster::bst_float> fvalue;
while (fscanf(file, "%s", tmp) == 1){
unsigned index; float value;
if (sscanf(tmp, "%u:%f", &index, &value) == 2){
findex.push_back(index); fvalue.push_back(value);
}
else{
if (!init){
info.labels.push_back(label);
data.AddRow(findex, fvalue);
}
findex.clear(); fvalue.clear();
utils::Assert(sscanf(tmp, "%f", &label) == 1, "invalid format");
init = false;
}
}
info.labels.push_back(label);
data.AddRow(findex, fvalue);
// initialize column support as well
data.InitData();
if (!silent){
printf("%ux%u matrix with %lu entries is loaded from %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
}
fclose(file);
this->TryLoadGroup(fname, silent);
this->TryLoadWeight(fname, silent);
}
/*!
* \brief load from binary file
* \param fname name of binary data
* \param silent whether print information or not
* \return whether loading is success
*/
inline bool LoadBinary(const char* fname, bool silent = false){
FILE *fp = fopen64(fname, "rb");
if (fp == NULL) return false;
utils::FileStream fs(fp);
data.LoadBinary(fs);
info.labels.resize(data.NumRow());
utils::Assert(fs.Read(&info.labels[0], sizeof(float)* data.NumRow()) != 0, "DMatrix LoadBinary");
{// load in group ptr
unsigned ngptr;
if( fs.Read(&ngptr, sizeof(unsigned) ) != 0 ){
info.group_ptr.resize( ngptr );
if( ngptr != 0 ){
utils::Assert( fs.Read(&info.group_ptr[0], sizeof(unsigned) * ngptr) != 0, "Load group file");
utils::Assert( info.group_ptr.back() == data.NumRow(), "number of group must match number of record" );
}
}
}
{// load in weight
unsigned nwt;
if( fs.Read(&nwt, sizeof(unsigned) ) != 0 ){
utils::Assert( nwt == 0 || nwt == data.NumRow(), "invalid weight" );
info.weights.resize( nwt );
if( nwt != 0 ){
utils::Assert( fs.Read(&info.weights[0], sizeof(unsigned) * nwt) != 0, "Load weight file");
}
}
}
fs.Close();
if (!silent){
printf("%ux%u matrix with %lu entries is loaded from %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
if( info.group_ptr.size() != 0 ){
printf("data contains %u groups\n", (unsigned)info.group_ptr.size()-1 );
}
}
return true;
}
/*!
* \brief save to binary file
* \param fname name of binary data
* \param silent whether print information or not
*/
inline void SaveBinary(const char* fname, bool silent = false){
// initialize column support as well
data.InitData();
utils::FileStream fs(utils::FopenCheck(fname, "wb"));
data.SaveBinary(fs);
utils::Assert( info.labels.size() == data.NumRow(), "label size is not consistent with feature matrix size" );
fs.Write(&info.labels[0], sizeof(float) * data.NumRow());
{// write out group ptr
unsigned ngptr = static_cast<unsigned>( info.group_ptr.size() );
fs.Write(&ngptr, sizeof(unsigned) );
if( ngptr != 0 ){
fs.Write(&info.group_ptr[0], sizeof(unsigned) * ngptr);
}
}
{// write out weight
unsigned nwt = static_cast<unsigned>( info.weights.size() );
fs.Write( &nwt, sizeof(unsigned) );
if( nwt != 0 ){
fs.Write(&info.weights[0], sizeof(float) * nwt);
}
}
fs.Close();
if (!silent){
printf("%ux%u matrix with %lu entries is saved to %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
if( info.group_ptr.size() != 0 ){
printf("data contains %u groups\n", (unsigned)info.group_ptr.size()-1 );
}
}
}
/*!
* \brief cache load data given a file name, if filename ends with .buffer, direct load binary
* otherwise the function will first check if fname + '.buffer' exists,
* if binary buffer exists, it will reads from binary buffer, otherwise, it will load from text file,
* and try to create a buffer file
* \param fname name of binary data
* \param silent whether print information or not
* \param savebuffer whether do save binary buffer if it is text
*/
inline void CacheLoad(const char *fname, bool silent = false, bool savebuffer = true){
int len = strlen(fname);
if (len > 8 && !strcmp(fname + len - 7, ".buffer")){
if( !this->LoadBinary(fname, silent) ){
fprintf(stderr,"can not open file \"%s\"", fname);
utils::Error("DMatrix::CacheLoad failed");
}
return;
}
char bname[1024];
sprintf(bname, "%s.buffer", fname);
if (!this->LoadBinary(bname, silent)){
this->LoadText(fname, silent);
if (savebuffer) this->SaveBinary(bname, silent);
}
}
private:
inline bool TryLoadGroup(const char* fname, bool silent = false){
std::string name = fname;
if (name.length() > 8 && !strcmp(fname + name.length() - 7, ".buffer")){
name.resize( name.length() - 7 );
}
name += ".group";
//if exists group data load it in
FILE *fi = fopen64(name.c_str(), "r");
if (fi == NULL) return false;
info.group_ptr.push_back(0);
unsigned nline;
while (fscanf(fi, "%u", &nline) == 1){
info.group_ptr.push_back(info.group_ptr.back()+nline);
}
if(!silent){
printf("%lu groups are loaded from %s\n", info.group_ptr.size()-1, name.c_str());
}
fclose(fi);
utils::Assert( info.group_ptr.back() == data.NumRow(), "DMatrix: group data does not match the number of rows in feature matrix" );
return true;
}
inline bool TryLoadWeight(const char* fname, bool silent = false){
std::string name = fname;
if (name.length() > 8 && !strcmp(fname + name.length() - 7, ".buffer")){
name.resize( name.length() - 7 );
}
name += ".weight";
//if exists group data load it in
FILE *fi = fopen64(name.c_str(), "r");
if (fi == NULL) return false;
float wt;
while (fscanf(fi, "%f", &wt) == 1){
info.weights.push_back( wt );
}
if(!silent){
printf("loading weight from %s\n", name.c_str());
}
fclose(fi);
utils::Assert( info.weights.size() == data.NumRow(), "DMatrix: weight data does not match the number of rows in feature matrix" );
return true;
}
};
};
};
#endif

375
regrank/xgboost_regrank_eval.h
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#ifndef XGBOOST_REGRANK_EVAL_H
#define XGBOOST_REGRANK_EVAL_H
/*!
* \file xgboost_regrank_eval.h
* \brief evaluation metrics for regression and classification and rank
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <cmath>
#include <vector>
#include <algorithm>
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_omp.h"
#include "../utils/xgboost_random.h"
#include "xgboost_regrank_data.h"
#include "xgboost_regrank_utils.h"
namespace xgboost{
namespace regrank{
/*! \brief evaluator that evaluates the loss metrics */
struct IEvaluator{
/*!
* \brief evaluate a specific metric
* \param preds prediction
* \param info information, including label etc.
*/
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const = 0;
/*! \return name of metric */
virtual const char *Name(void) const = 0;
/*! \brief virtual destructor */
virtual ~IEvaluator(void){}
};
/*! \brief RMSE */
struct EvalRMSE : public IEvaluator{
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
const unsigned ndata = static_cast<unsigned>(preds.size());
float sum = 0.0, wsum = 0.0;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float wt = info.GetWeight(i);
const float diff = info.labels[i] - preds[i];
sum += diff*diff * wt;
wsum += wt;
}
return sqrtf(sum / wsum);
}
virtual const char *Name(void) const{
return "rmse";
}
};
/*! \brief Error */
struct EvalLogLoss : public IEvaluator{
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
const unsigned ndata = static_cast<unsigned>(preds.size());
float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float y = info.labels[i];
const float py = preds[i];
const float wt = info.GetWeight(i);
sum -= wt * (y * std::log(py) + (1.0f - y)*std::log(1 - py));
wsum += wt;
}
return sum / wsum;
}
virtual const char *Name(void) const{
return "negllik";
}
};
/*! \brief Error */
struct EvalError : public IEvaluator{
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
const unsigned ndata = static_cast<unsigned>(preds.size());
float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float wt = info.GetWeight(i);
if (preds[i] > 0.5f){
if (info.labels[i] < 0.5f) sum += wt;
}
else{
if (info.labels[i] >= 0.5f) sum += wt;
}
wsum += wt;
}
return sum / wsum;
}
virtual const char *Name(void) const{
return "error";
}
};
/*! \brief AMS: also records best threshold */
struct EvalAMS : public IEvaluator{
public:
EvalAMS(const char *name){
name_ = name;
// note: ams@0 will automatically select which ratio to go
utils::Assert( sscanf(name, "ams@%f", &ratio_ ) == 1, "invalid ams format" );
}
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
const unsigned ndata = static_cast<unsigned>(preds.size());
utils::Assert( info.weights.size() == ndata, "we need weight to evaluate ams");
std::vector< std::pair<float, unsigned> > rec(ndata);
#pragma omp parallel for schedule( static )
for (unsigned i = 0; i < ndata; ++i){
rec[i] = std::make_pair( preds[i], i );
}
std::sort( rec.begin(), rec.end(), CmpFirst );
unsigned ntop = static_cast<unsigned>( ratio_ * ndata );
if( ntop == 0 ) ntop = ndata;
const double br = 10.0;
unsigned thresindex = 0;
double s_tp = 0.0, b_fp = 0.0, tams = 0.0;
for (unsigned i = 0; i < ndata-1 && i < ntop; ++i){
const unsigned ridx = rec[i].second;
const float wt = info.weights[ridx];
if( info.labels[ridx] > 0.5f ){
s_tp += wt;
}else{
b_fp += wt;
}
if( rec[i].first != rec[i+1].first ){
double ams = sqrtf( 2*((s_tp+b_fp+br) * log( 1.0 + s_tp/(b_fp+br) ) - s_tp) );
if( tams < ams ){
thresindex = i;
tams = ams;
}
}
}
if( ntop == ndata ){
fprintf( stderr, "\tams-ratio=%g", float(thresindex)/ndata );
return tams;
}else{
return sqrtf( 2*((s_tp+b_fp+br) * log( 1.0 + s_tp/(b_fp+br) ) - s_tp) );
}
}
virtual const char *Name(void) const{
return name_.c_str();
}
private:
std::string name_;
float ratio_;
};
/*! \brief Error for multi-class classification, need exact match */
struct EvalMatchError : public IEvaluator{
public:
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
const unsigned ndata = static_cast<unsigned>(preds.size());
float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float wt = info.GetWeight(i);
int label = static_cast<int>(info.labels[i]);
if (static_cast<int>(preds[i]) != label ) sum += wt;
wsum += wt;
}
return sum / wsum;
}
virtual const char *Name(void) const{
return "merror";
}
};
/*! \brief Area under curve, for both classification and rank */
struct EvalAuc : public IEvaluator{
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
std::vector<unsigned> tgptr(2, 0); tgptr[1] = preds.size();
const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
utils::Assert(gptr.back() == preds.size(), "EvalAuc: group structure must match number of prediction");
const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
double sum_auc = 0.0f;
#pragma omp parallel reduction(+:sum_auc)
{
// each thread takes a local rec
std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k){
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j){
rec.push_back(std::make_pair(preds[j], j));
}
std::sort(rec.begin(), rec.end(), CmpFirst);
// calculate AUC
double sum_pospair = 0.0;
double sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
for (size_t j = 0; j < rec.size(); ++j){
const float wt = info.GetWeight(rec[j].second);
const float ctr = info.labels[rec[j].second];
// keep bucketing predictions in same bucket
if (j != 0 && rec[j].first != rec[j - 1].first){
sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
sum_npos += buf_pos; sum_nneg += buf_neg;
buf_neg = buf_pos = 0.0f;
}
buf_pos += ctr * wt; buf_neg += (1.0f - ctr) * wt;
}
sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
sum_npos += buf_pos; sum_nneg += buf_neg;
//
utils::Assert(sum_npos > 0.0 && sum_nneg > 0.0, "the dataset only contains pos or neg samples");
// this is the AUC
sum_auc += sum_pospair / (sum_npos*sum_nneg);
}
}
// return average AUC over list
return static_cast<float>(sum_auc) / ngroup;
}
virtual const char *Name(void) const{
return "auc";
}
};
/*! \brief Evaluate rank list */
struct EvalRankList : public IEvaluator{
public:
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
const std::vector<unsigned> &gptr = info.group_ptr;
utils::Assert(gptr.size() != 0, "must specify group when constructing rank file");
utils::Assert( gptr.back() == preds.size(), "EvalRanklist: group structure must match number of prediction");
const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
double sum_metric = 0.0f;
#pragma omp parallel reduction(+:sum_metric)
{
// each thread takes a local rec
std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k){
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j){
rec.push_back(std::make_pair(preds[j], (int)info.labels[j]));
}
sum_metric += this->EvalMetric( rec );
}
}
return static_cast<float>(sum_metric) / ngroup;
}
virtual const char *Name(void) const{
return name_.c_str();
}
protected:
EvalRankList(const char *name){
name_ = name;
if( sscanf(name, "%*[^@]@%u", &topn_) != 1 ){
topn_ = UINT_MAX;
}
}
/*! \return evaluation metric, given the pair_sort record, (pred,label) */
virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &pair_sort ) const = 0;
protected:
unsigned topn_;
std::string name_;
};
/*! \brief Precison at N, for both classification and rank */
struct EvalPrecision : public EvalRankList{
public:
EvalPrecision(const char *name):EvalRankList(name){}
protected:
virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &rec ) const {
// calculate Preicsion
std::sort(rec.begin(), rec.end(), CmpFirst);
unsigned nhit = 0;
for (size_t j = 0; j < rec.size() && j < this->topn_; ++j){
nhit += (rec[j].second != 0 );
}
return static_cast<float>( nhit ) / topn_;
}
};
/*! \brief NDCG */
struct EvalNDCG : public EvalRankList{
public:
EvalNDCG(const char *name):EvalRankList(name){}
protected:
inline float CalcDCG( const std::vector< std::pair<float,unsigned> > &rec ) const {
double sumdcg = 0.0;
for( size_t i = 0; i < rec.size() && i < this->topn_; i ++ ){
const unsigned rel = rec[i].second;
if( rel != 0 ){
sumdcg += logf(2.0f) * ((1<<rel)-1) / logf( i + 2 );
}
}
return static_cast<float>(sumdcg);
}
virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &rec ) const {
std::sort(rec.begin(), rec.end(), CmpSecond);
float idcg = this->CalcDCG(rec);
std::sort(rec.begin(), rec.end(), CmpFirst);
float dcg = this->CalcDCG(rec);
if( idcg == 0.0f ) return 0.0f;
else return dcg/idcg;
}
};
/*! \brief Precison at N, for both classification and rank */
struct EvalMAP : public EvalRankList{
public:
EvalMAP(const char *name):EvalRankList(name){}
protected:
virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &rec ) const {
std::sort(rec.begin(), rec.end(), CmpFirst);
unsigned nhits = 0;
double sumap = 0.0;
for( size_t i = 0; i < rec.size(); ++i){
if( rec[i].second != 0 ){
nhits += 1;
if( i < this->topn_ ){
sumap += static_cast<float>(nhits) / (i+1);
}
}
}
if (nhits != 0) sumap /= nhits;
return static_cast<float>(sumap);
}
};
};
namespace regrank{
/*! \brief a set of evaluators */
struct EvalSet{
public:
inline void AddEval(const char *name){
for (size_t i = 0; i < evals_.size(); ++i){
if (!strcmp(name, evals_[i]->Name())) return;
}
if (!strcmp(name, "rmse")) evals_.push_back(new EvalRMSE());
if (!strcmp(name, "error")) evals_.push_back(new EvalError());
if (!strcmp(name, "merror")) evals_.push_back(new EvalMatchError());
if (!strcmp(name, "logloss")) evals_.push_back(new EvalLogLoss());
if (!strcmp(name, "auc")) evals_.push_back(new EvalAuc());
if (!strncmp(name, "ams@",4)) evals_.push_back(new EvalAMS(name));
if (!strncmp(name, "pre@", 4)) evals_.push_back(new EvalPrecision(name));
if (!strncmp(name, "map", 3)) evals_.push_back(new EvalMAP(name));
if (!strncmp(name, "ndcg", 3)) evals_.push_back(new EvalNDCG(name));
}
~EvalSet(){
for (size_t i = 0; i < evals_.size(); ++i){
delete evals_[i];
}
}
inline void Eval(FILE *fo, const char *evname,
const std::vector<float> &preds,
const DMatrix::Info &info) const{
for (size_t i = 0; i < evals_.size(); ++i){
float res = evals_[i]->Eval(preds, info);
fprintf(fo, "\t%s-%s:%f", evname, evals_[i]->Name(), res);
}
}
private:
std::vector<const IEvaluator*> evals_;
};
};
};
#endif

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regrank/xgboost_regrank_main.cpp
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#define _CRT_SECURE_NO_WARNINGS
#define _CRT_SECURE_NO_DEPRECATE
#include <ctime>
#include <string>
#include <cstring>
#include "xgboost_regrank.h"
#include "../utils/xgboost_fmap.h"
#include "../utils/xgboost_random.h"
#include "../utils/xgboost_config.h"
namespace xgboost{
namespace regrank{
/*!
* \brief wrapping the training process of the gradient boosting regression model,
* given the configuation
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.chen@gmail.com
*/
class RegBoostTask{
public:
inline int Run(int argc, char *argv[]){
if (argc < 2){
printf("Usage: <config>\n");
return 0;
}
utils::ConfigIterator itr(argv[1]);
while (itr.Next()){
this->SetParam(itr.name(), itr.val());
}
for (int i = 2; i < argc; i++){
char name[256], val[256];
if (sscanf(argv[i], "%[^=]=%s", name, val) == 2){
this->SetParam(name, val);
}
}
this->InitData();
this->InitLearner();
if (task == "dump"){
this->TaskDump();
return 0;
}
if (task == "interact"){
this->TaskInteractive(); return 0;
}
if (task == "dumppath"){
this->TaskDumpPath(); return 0;
}
if (task == "eval"){
this->TaskEval(); return 0;
}
if (task == "pred"){
this->TaskPred();
}
else{
this->TaskTrain();
}
return 0;
}
inline void SetParam(const char *name, const char *val){
if (!strcmp("silent", name)) silent = atoi(val);
if (!strcmp("use_buffer", name)) use_buffer = atoi(val);
if (!strcmp("seed", name)) random::Seed(atoi(val));
if (!strcmp("num_round", name)) num_round = atoi(val);
if (!strcmp("save_period", name)) save_period = atoi(val);
if (!strcmp("eval_train", name)) eval_train = atoi(val);
if (!strcmp("task", name)) task = val;
if (!strcmp("data", name)) train_path = val;
if (!strcmp("test:data", name)) test_path = val;
if (!strcmp("model_in", name)) model_in = val;
if (!strcmp("model_out", name)) model_out = val;
if (!strcmp("model_dir", name)) model_dir_path = val;
if (!strcmp("fmap", name)) name_fmap = val;
if (!strcmp("name_dump", name)) name_dump = val;
if (!strcmp("name_dumppath", name)) name_dumppath = val;
if (!strcmp("name_pred", name)) name_pred = val;
if (!strcmp("dump_stats", name)) dump_model_stats = atoi(val);
if (!strcmp("interact:action", name)) interact_action = val;
if (!strncmp("batch:", name, 6)){
cfg_batch.PushBack(name + 6, val);
}
if (!strncmp("eval[", name, 5)) {
char evname[256];
utils::Assert(sscanf(name, "eval[%[^]]", evname) == 1, "must specify evaluation name for display");
eval_data_names.push_back(std::string(evname));
eval_data_paths.push_back(std::string(val));
}
cfg.PushBack(name, val);
}
public:
RegBoostTask(void){
// default parameters
silent = 0;
use_buffer = 1;
num_round = 10;
save_period = 0;
eval_train = 0;
dump_model_stats = 0;
task = "train";
model_in = "NULL";
model_out = "NULL";
name_fmap = "NULL";
name_pred = "pred.txt";
name_dump = "dump.txt";
name_dumppath = "dump.path.txt";
model_dir_path = "./";
interact_action = "update";
}
~RegBoostTask(void){
for (size_t i = 0; i < deval.size(); i++){
delete deval[i];
}
}
private:
inline void InitData(void){
if (name_fmap != "NULL") fmap.LoadText(name_fmap.c_str());
if (task == "dump") return;
if (task == "pred" || task == "dumppath"){
data.CacheLoad(test_path.c_str(), silent != 0, use_buffer != 0);
}
else{
// training
data.CacheLoad(train_path.c_str(), silent != 0, use_buffer != 0);
utils::Assert(eval_data_names.size() == eval_data_paths.size());
for (size_t i = 0; i < eval_data_names.size(); ++i){
deval.push_back(new DMatrix());
deval.back()->CacheLoad(eval_data_paths[i].c_str(), silent != 0, use_buffer != 0);
devalall.push_back(deval.back());
}
std::vector<DMatrix *> dcache(1, &data);
for( size_t i = 0; i < deval.size(); ++ i){
dcache.push_back( deval[i] );
}
// set cache data to be all training and evaluation data
learner.SetCacheData(dcache);
// add training set to evaluation set if needed
if( eval_train != 0 ){
devalall.push_back( &data );
eval_data_names.push_back( std::string("train") );
}
}
}
inline void InitLearner(void){
cfg.BeforeFirst();
while (cfg.Next()){
learner.SetParam(cfg.name(), cfg.val());
}
if (model_in != "NULL"){
utils::FileStream fi(utils::FopenCheck(model_in.c_str(), "rb"));
learner.LoadModel(fi);
fi.Close();
}
else{
utils::Assert(task == "train", "model_in not specified");
learner.InitModel();
}
learner.InitTrainer();
}
inline void TaskTrain(void){
const time_t start = time(NULL);
unsigned long elapsed = 0;
for (int i = 0; i < num_round; ++i){
elapsed = (unsigned long)(time(NULL) - start);
if (!silent) printf("boosting round %d, %lu sec elapsed\n", i, elapsed);
learner.UpdateOneIter(data);
learner.EvalOneIter(i, devalall, eval_data_names);
if (save_period != 0 && (i + 1) % save_period == 0){
this->SaveModel(i);
}
elapsed = (unsigned long)(time(NULL) - start);
}
// always save final round
if ((save_period == 0 || num_round % save_period != 0) && model_out != "NONE"){
if (model_out == "NULL"){
this->SaveModel(num_round - 1);
}
else{
this->SaveModel(model_out.c_str());
}
}
if (!silent){
printf("\nupdating end, %lu sec in all\n", elapsed);
}
}
inline void TaskEval(void){
learner.EvalOneIter(0, devalall, eval_data_names);
}
inline void TaskInteractive(void){
const time_t start = time(NULL);
unsigned long elapsed = 0;
int batch_action = 0;
cfg_batch.BeforeFirst();
while (cfg_batch.Next()){
if (!strcmp(cfg_batch.name(), "run")){
learner.UpdateInteract(interact_action, data);
batch_action += 1;
}
else{
learner.SetParam(cfg_batch.name(), cfg_batch.val());
}
}
if (batch_action == 0){
learner.UpdateInteract(interact_action, data);
}
utils::Assert(model_out != "NULL", "interactive mode must specify model_out");
this->SaveModel(model_out.c_str());
elapsed = (unsigned long)(time(NULL) - start);
if (!silent){
printf("\ninteractive update, %d batch actions, %lu sec in all\n", batch_action, elapsed);
}
}
inline void TaskDump(void){
FILE *fo = utils::FopenCheck(name_dump.c_str(), "w");
learner.DumpModel(fo, fmap, dump_model_stats != 0);
fclose(fo);
}
inline void TaskDumpPath(void){
FILE *fo = utils::FopenCheck(name_dumppath.c_str(), "w");
learner.DumpPath(fo, data);
fclose(fo);
}
inline void SaveModel(const char *fname) const{
utils::FileStream fo(utils::FopenCheck(fname, "wb"));
learner.SaveModel(fo);
fo.Close();
}
inline void SaveModel(int i) const{
char fname[256];
sprintf(fname, "%s/%04d.model", model_dir_path.c_str(), i + 1);
this->SaveModel(fname);
}
inline void TaskPred(void){
std::vector<float> preds;
if (!silent) printf("start prediction...\n");
learner.Predict(preds, data);
if (!silent) printf("writing prediction to %s\n", name_pred.c_str());
FILE *fo = utils::FopenCheck(name_pred.c_str(), "w");
for (size_t i = 0; i < preds.size(); i++){
fprintf(fo, "%f\n", preds[i]);
}
fclose(fo);
}
private:
/* \brief whether silent */
int silent;
/* \brief whether use auto binary buffer */
int use_buffer;
/* \brief whether evaluate training statistics */
int eval_train;
/* \brief number of boosting iterations */
int num_round;
/* \brief the period to save the model, 0 means only save the final round model */
int save_period;
/*! \brief interfact action */
std::string interact_action;
/* \brief the path of training/test data set */
std::string train_path, test_path;
/* \brief the path of test model file, or file to restart training */
std::string model_in;
/* \brief the path of final model file, to be saved */
std::string model_out;
/* \brief the path of directory containing the saved models */
std::string model_dir_path;
/* \brief task to perform */
std::string task;
/* \brief name of predict file */
std::string name_pred;
/* \brief whether dump statistics along with model */
int dump_model_stats;
/* \brief name of feature map */
std::string name_fmap;
/* \brief name of dump file */
std::string name_dump;
/* \brief name of dump path file */
std::string name_dumppath;
/* \brief the paths of validation data sets */
std::vector<std::string> eval_data_paths;
/* \brief the names of the evaluation data used in output log */
std::vector<std::string> eval_data_names;
/*! \brief saves configurations */
utils::ConfigSaver cfg;
/*! \brief batch configurations */
utils::ConfigSaver cfg_batch;
private:
DMatrix data;
std::vector<DMatrix*> deval;
std::vector<const DMatrix*> devalall;
utils::FeatMap fmap;
RegRankBoostLearner learner;
};
};
};
int main( int argc, char *argv[] ){
xgboost::random::Seed( 0 );
xgboost::regrank::RegBoostTask tsk;
return tsk.Run( argc, argv );
}

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#ifndef XGBOOST_REGRANK_OBJ_H
#define XGBOOST_REGRANK_OBJ_H
/*!
* \file xgboost_regrank_obj.h
* \brief defines objective function interface used in xgboost for regression and rank
* \author Tianqi Chen, Kailong Chen
*/
#include "xgboost_regrank_data.h"
namespace xgboost{
namespace regrank{
/*! \brief interface of objective function */
class IObjFunction{
public:
/*! \brief virtual destructor */
virtual ~IObjFunction(void){}
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
virtual void SetParam(const char *name, const char *val) = 0;
/*!
* \brief get gradient over each of predictions, given existing information
* \param preds prediction of current round
* \param info information about labels, weights, groups in rank
* \param iter current iteration number
* \param grad gradient over each preds
* \param hess second order gradient over each preds
*/
virtual void GetGradient(const std::vector<float>& preds,
const DMatrix::Info &info,
int iter,
std::vector<float> &grad,
std::vector<float> &hess ) = 0;
/*! \return the default evaluation metric for the problem */
virtual const char* DefaultEvalMetric(void) = 0;
/*!
* \brief transform prediction values, this is only called when Prediction is called
* \param preds prediction values, saves to this vector as well
*/
virtual void PredTransform(std::vector<float> &preds){}
/*!
* \brief transform prediction values, this is only called when Eval is called, usually it redirect to PredTransform
* \param preds prediction values, saves to this vector as well
*/
virtual void EvalTransform(std::vector<float> &preds){ this->PredTransform(preds); }
};
};
namespace regrank{
/*! \brief defines functions to calculate some commonly used functions */
struct LossType{
public:
const static int kLinearSquare = 0;
const static int kLogisticNeglik = 1;
const static int kLogisticClassify = 2;
const static int kLogisticRaw = 3;
public:
/*! \brief indicate which type we are using */
int loss_type;
public:
/*!
* \brief transform the linear sum to prediction
* \param x linear sum of boosting ensemble
* \return transformed prediction
*/
inline float PredTransform(float x){
switch (loss_type){
case kLogisticRaw:
case kLinearSquare: return x;
case kLogisticClassify:
case kLogisticNeglik: return 1.0f / (1.0f + expf(-x));
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
/*!
* \brief calculate first order gradient of loss, given transformed prediction
* \param predt transformed prediction
* \param label true label
* \return first order gradient
*/
inline float FirstOrderGradient(float predt, float label) const{
switch (loss_type){
case kLinearSquare: return predt - label;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt - label;
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
/*!
* \brief calculate second order gradient of loss, given transformed prediction
* \param predt transformed prediction
* \param label true label
* \return second order gradient
*/
inline float SecondOrderGradient(float predt, float label) const{
switch (loss_type){
case kLinearSquare: return 1.0f;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt * (1 - predt);
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
};
};
};
#include "xgboost_regrank_obj.hpp"
namespace xgboost{
namespace regrank{
inline IObjFunction* CreateObjFunction( const char *name ){
if( !strcmp("reg:linear", name ) ) return new RegressionObj( LossType::kLinearSquare );
if( !strcmp("reg:logistic", name ) ) return new RegressionObj( LossType::kLogisticNeglik );
if( !strcmp("binary:logistic", name ) ) return new RegressionObj( LossType::kLogisticClassify );
if( !strcmp("binary:logitraw", name ) ) return new RegressionObj( LossType::kLogisticRaw );
if( !strcmp("multi:softmax", name ) ) return new SoftmaxMultiClassObj(0);
if( !strcmp("multi:softprob", name ) ) return new SoftmaxMultiClassObj(1);
if( !strcmp("rank:pairwise", name ) ) return new PairwiseRankObj();
if( !strcmp("rank:softmax", name ) ) return new SoftmaxRankObj();
utils::Error("unknown objective function type");
return NULL;
}
};
};
#endif

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#ifndef XGBOOST_REGRANK_OBJ_HPP
#define XGBOOST_REGRANK_OBJ_HPP
/*!
* \file xgboost_regrank_obj.hpp
* \brief implementation of objective functions
* \author Tianqi Chen, Kailong Chen
*/
//#include "xgboost_regrank_sample.h"
#include <vector>
#include <functional>
#include "xgboost_regrank_utils.h"
namespace xgboost{
namespace regrank{
class RegressionObj : public IObjFunction{
public:
RegressionObj( int loss_type ){
loss.loss_type = loss_type;
scale_pos_weight = 1.0f;
}
virtual ~RegressionObj(){}
virtual void SetParam(const char *name, const char *val){
if( !strcmp( "loss_type", name ) ) loss.loss_type = atoi( val );
if( !strcmp( "scale_pos_weight", name ) ) scale_pos_weight = (float)atof( val );
}
virtual void GetGradient(const std::vector<float>& preds,
const DMatrix::Info &info,
int iter,
std::vector<float> &grad,
std::vector<float> &hess ) {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
grad.resize(preds.size()); hess.resize(preds.size());
const unsigned ndata = static_cast<unsigned>(preds.size());
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
float p = loss.PredTransform(preds[j]);
float w = info.GetWeight(j);
if( info.labels[j] == 1.0f ) w *= scale_pos_weight;
grad[j] = loss.FirstOrderGradient(p, info.labels[j]) * w;
hess[j] = loss.SecondOrderGradient(p, info.labels[j]) * w;
}
}
virtual const char* DefaultEvalMetric(void) {
if( loss.loss_type == LossType::kLogisticClassify ) return "error";
if( loss.loss_type == LossType::kLogisticRaw ) return "auc";
return "rmse";
}
virtual void PredTransform(std::vector<float> &preds){
const unsigned ndata = static_cast<unsigned>(preds.size());
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
preds[j] = loss.PredTransform( preds[j] );
}
}
private:
float scale_pos_weight;
LossType loss;
};
};
namespace regrank{
// simple softmax rak
class SoftmaxRankObj : public IObjFunction{
public:
SoftmaxRankObj(void){
}
virtual ~SoftmaxRankObj(){}
virtual void SetParam(const char *name, const char *val){
}
virtual void GetGradient(const std::vector<float>& preds,
const DMatrix::Info &info,
int iter,
std::vector<float> &grad,
std::vector<float> &hess ) {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
grad.resize(preds.size()); hess.resize(preds.size());
const std::vector<unsigned> &gptr = info.group_ptr;
utils::Assert( gptr.size() != 0 && gptr.back() == preds.size(), "rank loss must have group file" );
const unsigned ngroup = static_cast<unsigned>( gptr.size() - 1 );
#pragma omp parallel
{
std::vector< float > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k){
rec.clear();
int nhit = 0;
for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
rec.push_back( preds[j] );
grad[j] = hess[j] = 0.0f;
nhit += info.labels[j];
}
Softmax( rec );
if( nhit == 1 ){
for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
float p = rec[ j - gptr[k] ];
grad[j] = p - info.labels[j];
hess[j] = 2.0f * p * ( 1.0f - p );
}
}else{
utils::Assert( nhit == 0, "softmax does not allow multiple labels" );
}
}
}
}
virtual const char* DefaultEvalMetric(void) {
return "pre@1";
}
};
// simple softmax multi-class classification
class SoftmaxMultiClassObj : public IObjFunction{
public:
SoftmaxMultiClassObj(int output_prob):output_prob(output_prob){
nclass = 0;
}
virtual ~SoftmaxMultiClassObj(){}
virtual void SetParam(const char *name, const char *val){
if( !strcmp( "num_class", name ) ) nclass = atoi(val);
}
virtual void GetGradient(const std::vector<float>& preds,
const DMatrix::Info &info,
int iter,
std::vector<float> &grad,
std::vector<float> &hess ) {
utils::Assert( nclass != 0, "must set num_class to use softmax" );
utils::Assert( preds.size() == (size_t)nclass * info.labels.size(), "SoftmaxMultiClassObj: label size and pred size does not match" );
grad.resize(preds.size()); hess.resize(preds.size());
const unsigned ndata = static_cast<unsigned>(info.labels.size());
#pragma omp parallel
{
std::vector<float> rec(nclass);
#pragma omp for schedule(static)
for (unsigned j = 0; j < ndata; ++j){
for( int k = 0; k < nclass; ++ k ){
rec[k] = preds[j + k * ndata];
}
Softmax( rec );
int label = static_cast<int>(info.labels[j]);
if( label < 0 ){
label = -label - 1;
}
utils::Assert( label < nclass, "SoftmaxMultiClassObj: label exceed num_class" );
for( int k = 0; k < nclass; ++ k ){
float p = rec[ k ];
if( label == k ){
grad[j+k*ndata] = p - 1.0f;
}else{
grad[j+k*ndata] = p;
}
hess[j+k*ndata] = 2.0f * p * ( 1.0f - p );
}
}
}
}
virtual void PredTransform(std::vector<float> &preds){
this->Transform(preds, output_prob);
}
virtual void EvalTransform(std::vector<float> &preds){
this->Transform(preds, 0);
}
private:
inline void Transform(std::vector<float> &preds, int prob){
utils::Assert( nclass != 0, "must set num_class to use softmax" );
utils::Assert( preds.size() % nclass == 0, "SoftmaxMultiClassObj: label size and pred size does not match" );
const unsigned ndata = static_cast<unsigned>(preds.size()/nclass);
#pragma omp parallel
{
std::vector<float> rec(nclass);
#pragma omp for schedule(static)
for (unsigned j = 0; j < ndata; ++j){
for( int k = 0; k < nclass; ++ k ){
rec[k] = preds[j + k * ndata];
}
if( prob == 0 ){
preds[j] = FindMaxIndex( rec );
}else{
Softmax( rec );
for( int k = 0; k < nclass; ++ k ){
preds[j + k * ndata] = rec[k];
}
}
}
}
if( prob == 0 ){
preds.resize( ndata );
}
}
virtual const char* DefaultEvalMetric(void) {
return "merror";
}
private:
int nclass;
int output_prob;
};
};
namespace regrank{
/*! \brief objective for lambda rank */
class LambdaRankObj : public IObjFunction{
public:
LambdaRankObj(void){
loss.loss_type = LossType::kLogisticRaw;
fix_list_weight = 0.0f;
num_pairsample = 1;
}
virtual ~LambdaRankObj(){}
virtual void SetParam(const char *name, const char *val){
if( !strcmp( "loss_type", name ) ) loss.loss_type = atoi( val );
if( !strcmp( "fix_list_weight", name ) ) fix_list_weight = (float)atof( val );
if( !strcmp( "num_pairsample", name ) ) num_pairsample = atoi( val );
}
public:
virtual void GetGradient(const std::vector<float>& preds,
const DMatrix::Info &info,
int iter,
std::vector<float> &grad,
std::vector<float> &hess ) {
utils::Assert( preds.size() == info.labels.size(), "label size predict size not match" );
grad.resize(preds.size()); hess.resize(preds.size());
const std::vector<unsigned> &gptr = info.group_ptr;
utils::Assert( gptr.size() != 0 && gptr.back() == preds.size(), "rank loss must have group file" );
const unsigned ngroup = static_cast<unsigned>( gptr.size() - 1 );
#pragma omp parallel
{
// parall construct, declare random number generator here, so that each
// thread use its own random number generator, seed by thread id and current iteration
random::Random rnd; rnd.Seed( iter * 1111 + omp_get_thread_num() );
std::vector<LambdaPair> pairs;
std::vector<ListEntry> lst;
std::vector< std::pair<float,unsigned> > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k){
lst.clear(); pairs.clear();
for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
lst.push_back( ListEntry(preds[j], info.labels[j], j ) );
grad[j] = hess[j] = 0.0f;
}
std::sort( lst.begin(), lst.end(), ListEntry::CmpPred );
rec.resize( lst.size() );
for( unsigned i = 0; i < lst.size(); ++i ){
rec[i] = std::make_pair( lst[i].label, i );
}
std::sort( rec.begin(), rec.end(), CmpFirst );
// enumerate buckets with same label, for each item in the lst, grab another sample randomly
for( unsigned i = 0; i < rec.size(); ){
unsigned j = i + 1;
while( j < rec.size() && rec[j].first == rec[i].first ) ++ j;
// bucket in [i,j), get a sample outside bucket
unsigned nleft = i, nright = rec.size() - j;
if( nleft + nright != 0 ){
int nsample = num_pairsample;
while( nsample -- ){
for( unsigned pid = i; pid < j; ++ pid ){
unsigned ridx = static_cast<unsigned>( rnd.RandDouble() * (nleft+nright) );
if( ridx < nleft ){
pairs.push_back( LambdaPair( rec[ridx].second, rec[pid].second ) );
}else{
pairs.push_back( LambdaPair( rec[pid].second, rec[ridx+j-i].second ) );
}
}
}
}
i = j;
}
// get lambda weight for the pairs
this->GetLambdaWeight( lst, pairs );
// rescale each gradient and hessian so that the lst have constant weighted
float scale = 1.0f / num_pairsample;
if( fix_list_weight != 0.0f ){
scale *= fix_list_weight / (gptr[k+1] - gptr[k]);
}
for( size_t i = 0; i < pairs.size(); ++ i ){
const ListEntry &pos = lst[ pairs[i].pos_index ];
const ListEntry &neg = lst[ pairs[i].neg_index ];
const float w = pairs[i].weight * scale;
float p = loss.PredTransform( pos.pred - neg.pred );
float g = loss.FirstOrderGradient( p, 1.0f );
float h = loss.SecondOrderGradient( p, 1.0f );
// accumulate gradient and hessian in both pid, and nid,
grad[ pos.rindex ] += g * w;
grad[ neg.rindex ] -= g * w;
// take conservative update, scale hessian by 2
hess[ pos.rindex ] += 2.0f * h * w;
hess[ neg.rindex ] += 2.0f * h * w;
}
}
}
}
virtual const char* DefaultEvalMetric(void) {
return "map";
}
private:
// loss function
LossType loss;
// number of samples peformed for each instance
int num_pairsample;
// fix weight of each elements in list
float fix_list_weight;
protected:
/*! \brief helper information in a list */
struct ListEntry{
/*! \brief the predict score we in the data */
float pred;
/*! \brief the actual label of the entry */
float label;
/*! \brief row index in the data matrix */
unsigned rindex;
// constructor
ListEntry(float pred, float label, unsigned rindex): pred(pred),label(label),rindex(rindex){}
// comparator by prediction
inline static bool CmpPred(const ListEntry &a, const ListEntry &b){
return a.pred > b.pred;
}
// comparator by label
inline static bool CmpLabel(const ListEntry &a, const ListEntry &b){
return a.label > b.label;
}
};
/*! \brief a pair in the lambda rank */
struct LambdaPair{
/*! \brief positive index: this is a position in the list */
unsigned pos_index;
/*! \brief negative index: this is a position in the list */
unsigned neg_index;
/*! \brief weight to be filled in */
float weight;
LambdaPair( unsigned pos_index, unsigned neg_index ):pos_index(pos_index),neg_index(neg_index),weight(1.0f){}
};
/*!
* \brief get lambda weight for existing pairs
* \param list a list that is sorted by pred score
* \param pairs record of pairs, containing the pairs to fill in weights
*/
virtual void GetLambdaWeight( const std::vector<ListEntry> &sorted_list, std::vector<LambdaPair> &pairs ) = 0;
};
};
namespace regrank{
class PairwiseRankObj: public LambdaRankObj{
public:
virtual ~PairwiseRankObj(void){}
virtual void GetLambdaWeight( const std::vector<ListEntry> &sorted_list, std::vector<LambdaPair> &pairs ){}
};
};
};
#endif

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#ifndef XGBOOST_REGRANK_UTILS_H
#define XGBOOST_REGRANK_UTILS_H
/*!
* \file xgboost_regrank_utils.h
* \brief useful helper functions
* \author Tianqi Chen, Kailong Chen
*/
namespace xgboost{
namespace regrank{
// simple helper function to do softmax
inline static void Softmax( std::vector<float>& rec ){
float wmax = rec[0];
for( size_t i = 1; i < rec.size(); ++ i ){
wmax = std::max( rec[i], wmax );
}
double wsum = 0.0f;
for( size_t i = 0; i < rec.size(); ++ i ){
rec[i] = expf(rec[i]-wmax);
wsum += rec[i];
}
for( size_t i = 0; i < rec.size(); ++ i ){
rec[i] /= static_cast<float>(wsum);
}
}
// simple helper function to do softmax
inline static int FindMaxIndex( std::vector<float>& rec ){
size_t mxid = 0;
for( size_t i = 1; i < rec.size(); ++ i ){
if( rec[i] > rec[mxid]+1e-6f ){
mxid = i;
}
}
return (int)mxid;
}
inline static bool CmpFirst(const std::pair<float, unsigned> &a, const std::pair<float, unsigned> &b){
return a.first > b.first;
}
inline static bool CmpSecond(const std::pair<float, unsigned> &a, const std::pair<float, unsigned> &b){
return a.second > b.second;
}
};
};
#endif

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#ifndef XGBOOST_TREE_MODEL_H_
#define XGBOOST_TREE_MODEL_H_
/*!
* \file model.h
* \brief model structure for tree
* \author Tianqi Chen
*/
#include <string>
#include <cstring>
#include <sstream>
#include <limits>
#include <algorithm>
#include <vector>
#include <cmath>
#include "../utils/io.h"
#include "../utils/fmap.h"
#include "../utils/utils.h"
namespace xgboost {
namespace tree {
/*!
* \brief template class of TreeModel
* \tparam TSplitCond data type to indicate split condition
* \tparam TNodeStat auxiliary statistics of node to help tree building
*/
template<typename TSplitCond, typename TNodeStat>
class TreeModel {
public:
/*! \brief data type to indicate split condition */
typedef TNodeStat NodeStat;
/*! \brief auxiliary statistics of node to help tree building */
typedef TSplitCond SplitCond;
/*! \brief parameters of the tree */
struct Param{
/*! \brief number of start root */
int num_roots;
/*! \brief total number of nodes */
int num_nodes;
/*!\brief number of deleted nodes */
int num_deleted;
/*! \brief maximum depth, this is a statistics of the tree */
int max_depth;
/*! \brief number of features used for tree construction */
int num_feature;
/*! \brief reserved part */
int reserved[32];
/*! \brief constructor */
Param(void) {
max_depth = 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("num_roots", name)) num_roots = atoi(val);
if (!strcmp("num_feature", name)) num_feature = atoi(val);
}
};
/*! \brief tree node */
class Node{
public:
/*! \brief index of left child */
inline int cleft(void) const {
return this->cleft_;
}
/*! \brief index of right child */
inline int cright(void) const {
return this->cright_;
}
/*! \brief index of default child when feature is missing */
inline int cdefault(void) const {
return this->default_left() ? this->cleft() : this->cright();
}
/*! \brief feature index of split condition */
inline unsigned split_index(void) const {
return sindex_ & ((1U << 31) - 1U);
}
/*! \brief when feature is unknown, whether goes to left child */
inline bool default_left(void) const {
return (sindex_ >> 31) != 0;
}
/*! \brief whether current node is leaf node */
inline bool is_leaf(void) const {
return cleft_ == -1;
}
/*! \brief get leaf value of leaf node */
inline float leaf_value(void) const {
return (this->info_).leaf_value;
}
/*! \brief get split condition of the node */
inline TSplitCond split_cond(void) const {
return (this->info_).split_cond;
}
/*! \brief get parent of the node */
inline int parent(void) const {
return parent_ & ((1U << 31) - 1);
}
/*! \brief whether current node is left child */
inline bool is_left_child(void) const {
return (parent_ & (1U << 31)) != 0;
}
/*! \brief whether current node is root */
inline bool is_root(void) const {
return parent_ == -1;
}
/*!
* \brief set the right child
* \param nide node id to right child
*/
inline void set_right_child(int nid) {
this->cright_ = nid;
}
/*!
* \brief set split condition of current node
* \param split_index feature index to split
* \param split_cond split condition
* \param default_left the default direction when feature is unknown
*/
inline void set_split(unsigned split_index, TSplitCond split_cond,
bool default_left = false) {
if (default_left) split_index |= (1U << 31);
this->sindex_ = split_index;
(this->info_).split_cond = split_cond;
}
/*!
* \brief set the leaf value of the node
* \param value leaf value
* \param right right index, could be used to store
* additional information
*/
inline void set_leaf(float value, int right = -1) {
(this->info_).leaf_value = value;
this->cleft_ = -1;
this->cright_ = right;
}
private:
friend class TreeModel<TSplitCond, TNodeStat>;
/*!
* \brief in leaf node, we have weights, in non-leaf nodes,
* we have split condition
*/
union Info{
float leaf_value;
TSplitCond split_cond;
};
// pointer to parent, highest bit is used to
// indicate whether it's a left child or not
int parent_;
// pointer to left, right
int cleft_, cright_;
// split feature index, left split or right split depends on the highest bit
unsigned sindex_;
// extra info
Info info_;
// set parent
inline void set_parent(int pidx, bool is_left_child = true) {
if (is_left_child) pidx |= (1U << 31);
this->parent_ = pidx;
}
};
protected:
// vector of nodes
std::vector<Node> nodes;
// stats of nodes
std::vector<TNodeStat> stats;
// free node space, used during training process
std::vector<int> deleted_nodes;
// allocate a new node,
// !!!!!! NOTE: may cause BUG here, nodes.resize
inline int AllocNode(void) {
if (param.num_deleted != 0) {
int nd = deleted_nodes.back();
deleted_nodes.pop_back();
--param.num_deleted;
return nd;
}
int nd = param.num_nodes++;
utils::Check(param.num_nodes < std::numeric_limits<int>::max(),
"number of nodes in the tree exceed 2^31");
nodes.resize(param.num_nodes);
stats.resize(param.num_nodes);
return nd;
}
// delete a tree node
inline void DeleteNode(int nid) {
utils::Assert(nid >= param.num_roots, "can not delete root");
deleted_nodes.push_back(nid);
nodes[nid].set_parent(-1);
++param.num_deleted;
}
public:
/*!
* \brief change a non leaf node to a leaf node, delete its children
* \param rid node id of the node
* \param new leaf value
*/
inline void ChangeToLeaf(int rid, float value) {
utils::Assert(nodes[nodes[rid].cleft() ].is_leaf(),
"can not delete a non termial child");
utils::Assert(nodes[nodes[rid].cright()].is_leaf(),
"can not delete a non termial child");
this->DeleteNode(nodes[rid].cleft());
this->DeleteNode(nodes[rid].cright());
nodes[rid].set_leaf(value);
}
/*!
* \brief collapse a non leaf node to a leaf node, delete its children
* \param rid node id of the node
* \param new leaf value
*/
inline void CollapseToLeaf(int rid, float value) {
if (nodes[rid].is_leaf()) return;
if (!nodes[nodes[rid].cleft() ].is_leaf()) {
CollapseToLeaf(nodes[rid].cleft(), 0.0f);
}
if (!nodes[nodes[rid].cright() ].is_leaf()) {
CollapseToLeaf(nodes[rid].cright(), 0.0f);
}
this->ChangeToLeaf(rid, value);
}
public:
/*! \brief model parameter */
Param param;
/*! \brief constructor */
TreeModel(void) {
param.num_nodes = 1;
param.num_roots = 1;
param.num_deleted = 0;
nodes.resize(1);
}
/*! \brief get node given nid */
inline Node &operator[](int nid) {
return nodes[nid];
}
/*! \brief get node given nid */
inline const Node &operator[](int nid) const {
return nodes[nid];
}
/*! \brief get node statistics given nid */
inline NodeStat &stat(int nid) {
return stats[nid];
}
/*! \brief initialize the model */
inline void InitModel(void) {
param.num_nodes = param.num_roots;
nodes.resize(param.num_nodes);
stats.resize(param.num_nodes);
for (int i = 0; i < param.num_nodes; i ++) {
nodes[i].set_leaf(0.0f);
nodes[i].set_parent(-1);
}
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void LoadModel(utils::IStream &fi) {
utils::Check(fi.Read(&param, sizeof(Param)) > 0,
"TreeModel: wrong format");
nodes.resize(param.num_nodes); stats.resize(param.num_nodes);
utils::Check(fi.Read(&nodes[0], sizeof(Node) * nodes.size()) > 0,
"TreeModel: wrong format");
utils::Check(fi.Read(&stats[0], sizeof(NodeStat) * stats.size()) > 0,
"TreeModel: wrong format");
// chg deleted nodes
deleted_nodes.resize(0);
for (int i = param.num_roots; i < param.num_nodes; i ++) {
if (nodes[i].is_root()) deleted_nodes.push_back(i);
}
utils::Assert(static_cast<int>(deleted_nodes.size()) == param.num_deleted,
"number of deleted nodes do not match");
}
/*!
* \brief save model to stream
* \param fo output stream
*/
inline void SaveModel(utils::IStream &fo) const {
utils::Assert(param.num_nodes == static_cast<int>(nodes.size()),
"Tree::SaveModel");
utils::Assert(param.num_nodes == static_cast<int>(stats.size()),
"Tree::SaveModel");
fo.Write(&param, sizeof(Param));
fo.Write(&nodes[0], sizeof(Node) * nodes.size());
fo.Write(&stats[0], sizeof(NodeStat) * nodes.size());
}
/*!
* \brief add child nodes to node
* \param nid node id to add childs
*/
inline void AddChilds(int nid) {
int pleft = this->AllocNode();
int pright = this->AllocNode();
nodes[nid].cleft_ = pleft;
nodes[nid].cright_ = pright;
nodes[nodes[nid].cleft() ].set_parent(nid, true);
nodes[nodes[nid].cright()].set_parent(nid, false);
}
/*!
* \brief only add a right child to a leaf node
* \param node id to add right child
*/
inline void AddRightChild(int nid) {
int pright = this->AllocNode();
nodes[nid].right = pright;
nodes[nodes[nid].right].set_parent(nid, false);
}
/*!
* \brief get current depth
* \param nid node id
* \param pass_rchild whether right child is not counted in depth
*/
inline int GetDepth(int nid, bool pass_rchild = false) const {
int depth = 0;
while (!nodes[nid].is_root()) {
if (!pass_rchild || nodes[nid].is_left_child()) ++depth;
nid = nodes[nid].parent();
}
return depth;
}
/*!
* \brief get maximum depth
* \param nid node id
*/
inline int MaxDepth(int nid) const {
if (nodes[nid].is_leaf()) return 0;
return std::max(MaxDepth(nodes[nid].cleft())+1,
MaxDepth(nodes[nid].cright())+1);
}
/*!
* \brief get maximum depth
*/
inline int MaxDepth(void) {
int maxd = 0;
for (int i = 0; i < param.num_roots; ++i) {
maxd = std::max(maxd, MaxDepth(i));
}
return maxd;
}
/*! \brief number of extra nodes besides the root */
inline int num_extra_nodes(void) const {
return param.num_nodes - param.num_roots - param.num_deleted;
}
/*!
* \brief dump model to text string
* \param fmap feature map of feature types
* \param with_stats whether dump out statistics as well
* \return the string of dumped model
*/
inline std::string DumpModel(const utils::FeatMap& fmap, bool with_stats) {
std::stringstream fo("");
for (int i = 0; i < param.num_roots; ++i) {
this->Dump(i, fo, fmap, 0, with_stats);
}
return fo.str();
}
private:
void Dump(int nid, std::stringstream &fo,
const utils::FeatMap& fmap, int depth, bool with_stats) {
for (int i = 0; i < depth; ++i) {
fo << '\t';
}
if (nodes[nid].is_leaf()) {
fo << nid << ":leaf=" << nodes[nid].leaf_value();
if (with_stats) {
stat(nid).Print(fo, true);
}
fo << '\n';
} else {
// right then left,
TSplitCond cond = nodes[nid].split_cond();
const unsigned split_index = nodes[nid].split_index();
if (split_index < fmap.size()) {
switch (fmap.type(split_index)) {
case utils::FeatMap::kIndicator: {
int nyes = nodes[nid].default_left() ?
nodes[nid].cright() : nodes[nid].cleft();
fo << nid << ":[" << fmap.name(split_index) << "] yes=" << nyes
<< ",no=" << nodes[nid].cdefault();
break;
}
case utils::FeatMap::kInteger: {
fo << nid << ":[" << fmap.name(split_index) << "<"
<< int(float(cond)+1.0f)
<< "] yes=" << nodes[nid].cleft()
<< ",no=" << nodes[nid].cright()
<< ",missing=" << nodes[nid].cdefault();
break;
}
case utils::FeatMap::kFloat:
case utils::FeatMap::kQuantitive: {
fo << nid << ":[" << fmap.name(split_index) << "<"<< float(cond)
<< "] yes=" << nodes[nid].cleft()
<< ",no=" << nodes[nid].cright()
<< ",missing=" << nodes[nid].cdefault();
break;
}
default: utils::Error("unknown fmap type");
}
} else {
fo << nid << ":[f" << split_index << "<"<< float(cond)
<< "] yes=" << nodes[nid].cleft()
<< ",no=" << nodes[nid].cright()
<< ",missing=" << nodes[nid].cdefault();
}
if (with_stats) {
fo << ' ';
stat(nid).Print(fo, false);
}
fo << '\n';
this->Dump(nodes[nid].cleft(), fo, fmap, depth+1, with_stats);
this->Dump(nodes[nid].cright(), fo, fmap, depth+1, with_stats);
}
}
};
/*! \brief node statistics used in regression tree */
struct RTreeNodeStat{
/*! \brief loss chg caused by current split */
float loss_chg;
/*! \brief sum of hessian values, used to measure coverage of data */
float sum_hess;
/*! \brief weight of current node */
float base_weight;
/*! \brief number of child that is leaf node known up to now */
int leaf_child_cnt;
/*! \brief print information of current stats to fo */
inline void Print(std::stringstream &fo, bool is_leaf) const {
if (!is_leaf) {
fo << "gain=" << loss_chg << ",cover=" << sum_hess;
} else {
fo << "cover=" << sum_hess;
}
}
};
/*! \brief define regression tree to be the most common tree model */
class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
public:
/*!
* \brief get the leaf index
* \param feats dense feature vector, if the feature is missing the field is set to NaN
* \param root_gid starting root index of the instance
* \return the leaf index of the given feature
*/
inline int GetLeafIndex(const std::vector<float> &feat, unsigned root_id = 0) const {
// start from groups that belongs to current data
int pid = static_cast<int>(root_id);
// tranverse tree
while (!(*this)[ pid ].is_leaf()) {
unsigned split_index = (*this)[pid].split_index();
const float fvalue = feat[split_index];
pid = this->GetNext(pid, fvalue, std::isnan(fvalue));
}
return pid;
}
/*!
* \brief get the prediction of regression tree, only accepts dense feature vector
* \param feats dense feature vector, if the feature is missing the field is set to NaN
* \param root_gid starting root index of the instance
* \return the leaf index of the given feature
*/
inline float Predict(const std::vector<float> &feat, unsigned root_id = 0) const {
int pid = this->GetLeafIndex(feat, root_id);
return (*this)[pid].leaf_value();
}
private:
/*! \brief get next position of the tree given current pid */
inline int GetNext(int pid, float fvalue, bool is_unknown) const {
float split_value = (*this)[pid].split_cond();
if (is_unknown) {
return (*this)[pid].cdefault();
} else {
if (fvalue < split_value) {
return (*this)[pid].cleft();
} else {
return (*this)[pid].cright();
}
}
}
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_MODEL_H_

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#ifndef XGBOOST_TREE_PARAM_H_
#define XGBOOST_TREE_PARAM_H_
/*!
* \file param.h
* \brief training parameters, statistics used to support tree construction
* \author Tianqi Chen
*/
#include <cstring>
#include "../data.h"
namespace xgboost {
namespace tree {
/*! \brief core statistics used for tree construction */
struct GradStats {
/*! \brief sum gradient statistics */
double sum_grad;
/*! \brief sum hessian statistics */
double sum_hess;
/*! \brief constructor */
GradStats(void) {
this->Clear();
}
/*! \brief clear the statistics */
inline void Clear(void) {
sum_grad = sum_hess = 0.0f;
}
/*! \brief add statistics to the data */
inline void Add(double grad, double hess) {
sum_grad += grad; sum_hess += hess;
}
/*! \brief add statistics to the data */
inline void Add(const bst_gpair& b) {
this->Add(b.grad, b.hess);
}
/*! \brief add statistics to the data */
inline void Add(const GradStats &b) {
this->Add(b.sum_grad, b.sum_hess);
}
/*! \brief substract the statistics by b */
inline GradStats Substract(const GradStats &b) const {
GradStats res;
res.sum_grad = this->sum_grad - b.sum_grad;
res.sum_hess = this->sum_hess - b.sum_hess;
return res;
}
/*! \return whether the statistics is not used yet */
inline bool Empty(void) const {
return sum_hess == 0.0;
}
};
/*! \brief training parameters for regression tree */
struct TrainParam{
// learning step size for a time
float learning_rate;
// minimum loss change required for a split
float min_split_loss;
// maximum depth of a tree
int max_depth;
//----- the rest parameters are less important ----
// minimum amount of hessian(weight) allowed in a child
float min_child_weight;
// weight decay parameter used to control leaf fitting
float reg_lambda;
// reg method
int reg_method;
// default direction choice
int default_direction;
// whether we want to do subsample
float subsample;
// whether to subsample columns each split, in each level
float colsample_bylevel;
// whether to subsample columns during tree construction
float colsample_bytree;
// speed optimization for dense column
float opt_dense_col;
// number of threads to be used for tree construction,
// if OpenMP is enabled, if equals 0, use system default
int nthread;
/*! \brief constructor */
TrainParam(void) {
learning_rate = 0.3f;
min_child_weight = 1.0f;
max_depth = 6;
reg_lambda = 1.0f;
reg_method = 2;
default_direction = 0;
subsample = 1.0f;
colsample_bytree = 1.0f;
colsample_bylevel = 1.0f;
opt_dense_col = 1.0f;
nthread = 0;
}
/*!
* \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) {
// sync-names
if (!strcmp(name, "gamma")) min_split_loss = static_cast<float>(atof(val));
if (!strcmp(name, "eta")) learning_rate = static_cast<float>(atof(val));
if (!strcmp(name, "lambda")) reg_lambda = static_cast<float>(atof(val));
if (!strcmp(name, "learning_rate")) learning_rate = static_cast<float>(atof(val));
if (!strcmp(name, "min_child_weight")) min_child_weight = static_cast<float>(atof(val));
if (!strcmp(name, "min_split_loss")) min_split_loss = static_cast<float>(atof(val));
if (!strcmp(name, "reg_lambda")) reg_lambda = static_cast<float>(atof(val));
if (!strcmp(name, "reg_method")) reg_method = static_cast<float>(atof(val));
if (!strcmp(name, "subsample")) subsample = static_cast<float>(atof(val));
if (!strcmp(name, "colsample_bylevel")) colsample_bylevel = static_cast<float>(atof(val));
if (!strcmp(name, "colsample_bytree")) colsample_bytree = static_cast<float>(atof(val));
if (!strcmp(name, "opt_dense_col")) opt_dense_col = static_cast<float>(atof(val));
if (!strcmp(name, "max_depth")) max_depth = atoi(val);
if (!strcmp(name, "nthread")) nthread = atoi(val);
if (!strcmp(name, "default_direction")) {
if (!strcmp(val, "learn")) default_direction = 0;
if (!strcmp(val, "left")) default_direction = 1;
if (!strcmp(val, "right")) default_direction = 2;
}
}
// calculate the cost of loss function
inline double CalcGain(double sum_grad, double sum_hess) const {
if (sum_hess < min_child_weight) {
return 0.0;
}
switch (reg_method) {
case 1 : return Sqr(ThresholdL1(sum_grad, reg_lambda)) / sum_hess;
case 2 : return Sqr(sum_grad) / (sum_hess + reg_lambda);
case 3 : return
Sqr(ThresholdL1(sum_grad, 0.5 * reg_lambda)) /
(sum_hess + 0.5 * reg_lambda);
default: return Sqr(sum_grad) / sum_hess;
}
}
// calculate weight given the statistics
inline double CalcWeight(double sum_grad, double sum_hess) const {
if (sum_hess < min_child_weight) {
return 0.0;
} else {
switch (reg_method) {
case 1: return - ThresholdL1(sum_grad, reg_lambda) / sum_hess;
case 2: return - sum_grad / (sum_hess + reg_lambda);
case 3: return
- ThresholdL1(sum_grad, 0.5 * reg_lambda) /
(sum_hess + 0.5 * reg_lambda);
default: return - sum_grad / sum_hess;
}
}
}
/*! \brief whether need forward small to big search: default right */
inline bool need_forward_search(float col_density = 0.0f) const {
return this->default_direction == 2 ||
(default_direction == 0 && (col_density < opt_dense_col));
}
/*! \brief whether need backward big to small search: default left */
inline bool need_backward_search(float col_density = 0.0f) const {
return this->default_direction != 2;
}
/*! \brief given the loss change, whether we need to invode prunning */
inline bool need_prune(double loss_chg, int depth) const {
return loss_chg < this->min_split_loss;
}
/*! \brief whether we can split with current hessian */
inline bool cannot_split(double sum_hess, int depth) const {
return sum_hess < this->min_child_weight * 2.0;
}
// code support for template data
inline double CalcWeight(const GradStats &d) const {
return this->CalcWeight(d.sum_grad, d.sum_hess);
}
inline double CalcGain(const GradStats &d) const {
return this->CalcGain(d.sum_grad, d.sum_hess);
}
protected:
// functions for L1 cost
inline static double ThresholdL1(double w, double lambda) {
if (w > +lambda) return w - lambda;
if (w < -lambda) return w + lambda;
return 0.0;
}
inline static double Sqr(double a) {
return a * a;
}
};
/*!
* \brief statistics that is helpful to store
* and represent a split solution for the tree
*/
struct SplitEntry{
/*! \brief loss change after split this node */
bst_float loss_chg;
/*! \brief split index */
unsigned sindex;
/*! \brief split value */
float split_value;
/*! \brief constructor */
SplitEntry(void) : loss_chg(0.0f), sindex(0), split_value(0.0f) {}
/*!
* \brief decides whether a we can replace current entry with the statistics given
* This function gives better priority to lower index when loss_chg equals
* not the best way, but helps to give consistent result during multi-thread execution
* \param loss_chg the loss reduction get through the split
* \param split_index the feature index where the split is on
*/
inline bool NeedReplace(bst_float loss_chg, unsigned split_index) const {
if (this->split_index() <= split_index) {
return loss_chg > this->loss_chg;
} else {
return !(this->loss_chg > loss_chg);
}
}
/*!
* \brief update the split entry, replace it if e is better
* \param e candidate split solution
* \return whether the proposed split is better and can replace current split
*/
inline bool Update(const SplitEntry &e) {
if (this->NeedReplace(e.loss_chg, e.split_index())) {
this->loss_chg = e.loss_chg;
this->sindex = e.sindex;
this->split_value = e.split_value;
return true;
} else {
return false;
}
}
/*!
* \brief update the split entry, replace it if e is better
* \param loss_chg loss reduction of new candidate
* \param split_index feature index to split on
* \param split_value the split point
* \param default_left whether the missing value goes to left
* \return whether the proposed split is better and can replace current split
*/
inline bool Update(bst_float loss_chg, unsigned split_index,
float split_value, bool default_left) {
if (this->NeedReplace(loss_chg, split_index)) {
this->loss_chg = loss_chg;
if (default_left) split_index |= (1U << 31);
this->sindex = split_index;
this->split_value = split_value;
return true;
} else {
return false;
}
}
/*!\return feature index to split on */
inline unsigned split_index(void) const {
return sindex & ((1U << 31) - 1U);
}
/*!\return whether missing value goes to left branch */
inline bool default_left(void) const {
return (sindex >> 31) != 0;
}
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_PARAM_H_

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#ifndef XGBOOST_TREE_UPDATER_H_
#define XGBOOST_TREE_UPDATER_H_
/*!
* \file updater.h
* \brief interface to update the tree
* \author Tianqi Chen
*/
#include <vector>
#include "../data.h"
#include "./model.h"
namespace xgboost {
namespace tree {
/*!
* \brief interface of tree update module, that performs update of a tree
* \tparam FMatrix the data type updater taking
*/
template<typename FMatrix>
class IUpdater {
public:
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
virtual void SetParam(const char *name, const char *val) = 0;
/*!
* \brief peform update to the tree models
* \param gpair the gradient pair statistics of the data
* \param fmat feature matrix that provide access to features
* \param root_index pre-partitioned root_index of each instance,
* root_index.size() can be 0 which indicates that no pre-partition involved
* \param trees pointer to the trese to be updated, upater will change the content of the tree
* note: all the trees in the vector are updated, with the same statistics,
* but maybe different random seeds, usually one tree is passed in at a time,
* there can be multiple trees when we train random forest style model
*/
virtual void Update(const std::vector<bst_gpair> &gpair,
FMatrix &fmat,
const std::vector<unsigned> &root_index,
const std::vector<RegTree*> &trees) = 0;
// destructor
virtual ~IUpdater(void) {}
};
} // namespace tree
} // namespace xgboost
#include "./updater_prune-inl.hpp"
#include "./updater_colmaker-inl.hpp"
namespace xgboost {
namespace tree {
/*!
* \brief create a updater based on name
* \param name name of updater
* \return return the updater instance
*/
template<typename FMatrix>
inline IUpdater<FMatrix>* CreateUpdater(const char *name) {
if (!strcmp(name, "prune")) return new TreePruner<FMatrix>();
if (!strcmp(name, "grow_colmaker")) return new ColMaker<FMatrix, GradStats>();
utils::Error("unknown updater:%s", name);
return NULL;
}
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_UPDATER_H_

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#ifndef XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_
#define XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_
/*!
* \file updater_colmaker-inl.hpp
* \brief use columnwise update to construct a tree
* \author Tianqi Chen
*/
#include <vector>
#include <algorithm>
#include "./param.h"
#include "./updater.h"
#include "../utils/omp.h"
#include "../utils/random.h"
namespace xgboost {
namespace tree {
/*! \brief pruner that prunes a tree after growing finishs */
template<typename FMatrix, typename TStats>
class ColMaker: public IUpdater<FMatrix> {
public:
virtual ~ColMaker(void) {}
// set training parameter
virtual void SetParam(const char *name, const char *val) {
param.SetParam(name, val);
}
virtual void Update(const std::vector<bst_gpair> &gpair,
FMatrix &fmat,
const std::vector<unsigned> &root_index,
const std::vector<RegTree*> &trees) {
fmat.InitColAccess();
for (size_t i = 0; i < trees.size(); ++i) {
Builder builder(param);
builder.Update(gpair, fmat, root_index, trees[i]);
}
}
private:
// training parameter
TrainParam param;
// data structure
/*! \brief per thread x per node entry to store tmp data */
struct ThreadEntry {
/*! \brief statistics of data*/
TStats stats;
/*! \brief last feature value scanned */
float last_fvalue;
/*! \brief current best solution */
SplitEntry best;
// constructor
ThreadEntry(void) {
stats.Clear();
}
};
struct NodeEntry {
/*! \brief statics for node entry */
TStats stats;
/*! \brief loss of this node, without split */
bst_float root_gain;
/*! \brief weight calculated related to current data */
float weight;
/*! \brief current best solution */
SplitEntry best;
// constructor
NodeEntry(void) : root_gain(0.0f), weight(0.0f){
stats.Clear();
}
};
// actual builder that runs the algorithm
struct Builder{
public:
// constructor
explicit Builder(const TrainParam &param) : param(param) {}
// update one tree, growing
virtual void Update(const std::vector<bst_gpair> &gpair, FMatrix &fmat,
const std::vector<unsigned> &root_index,
RegTree *p_tree) {
this->InitData(gpair, fmat, root_index, *p_tree);
this->InitNewNode(qexpand, gpair, *p_tree);
for (int depth = 0; depth < param.max_depth; ++depth) {
this->FindSplit(depth, this->qexpand, gpair, fmat, p_tree);
this->ResetPosition(this->qexpand, fmat, *p_tree);
this->UpdateQueueExpand(*p_tree, &this->qexpand);
this->InitNewNode(qexpand, gpair, *p_tree);
// if nothing left to be expand, break
if (qexpand.size() == 0) break;
}
// set all the rest expanding nodes to leaf
for (size_t i = 0; i < qexpand.size(); ++i) {
const int nid = qexpand[i];
(*p_tree)[nid].set_leaf(snode[nid].weight * param.learning_rate);
}
// remember auxiliary statistics in the tree node
for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
p_tree->stat(nid).loss_chg = snode[nid].best.loss_chg;
p_tree->stat(nid).base_weight = snode[nid].weight;
p_tree->stat(nid).sum_hess = static_cast<float>(snode[nid].stats.sum_hess);
}
}
private:
// initialize temp data structure
inline void InitData(const std::vector<bst_gpair> &gpair, FMatrix &fmat,
const std::vector<unsigned> &root_index, const RegTree &tree) {
utils::Assert(tree.param.num_nodes == tree.param.num_roots, "ColMaker: can only grow new tree");
{// setup position
position.resize(gpair.size());
if (root_index.size() == 0) {
std::fill(position.begin(), position.end(), 0);
} else {
for (size_t i = 0; i < root_index.size(); ++i) {
position[i] = root_index[i];
utils::Assert(root_index[i] < (unsigned)tree.param.num_roots, "root index exceed setting");
}
}
// mark delete for the deleted datas
for (size_t i = 0; i < gpair.size(); ++i) {
if (gpair[i].hess < 0.0f) position[i] = -1;
}
// mark subsample
if (param.subsample < 1.0f) {
for (size_t i = 0; i < gpair.size(); ++i) {
if (gpair[i].hess < 0.0f) continue;
if (random::SampleBinary(param.subsample) == 0) position[i] = -1;
}
}
}
{
// initialize feature index
unsigned ncol = static_cast<unsigned>(fmat.NumCol());
for (unsigned i = 0; i < ncol; ++i) {
if (fmat.GetColSize(i) != 0) feat_index.push_back(i);
}
unsigned n = static_cast<unsigned>(param.colsample_bytree * feat_index.size());
random::Shuffle(feat_index);
utils::Check(n > 0, "colsample_bytree is too small that no feature can be included");
feat_index.resize(n);
}
{// setup temp space for each thread
#pragma omp parallel
{
this->nthread = omp_get_num_threads();
}
// reserve a small space
stemp.clear();
stemp.resize(this->nthread, std::vector<ThreadEntry>());
for (size_t i = 0; i < stemp.size(); ++i) {
stemp[i].clear(); stemp[i].reserve(256);
}
snode.reserve(256);
}
{// expand query
qexpand.reserve(256); qexpand.clear();
for (int i = 0; i < tree.param.num_roots; ++i) {
qexpand.push_back(i);
}
}
}
/*! \brief initialize the base_weight, root_gain, and NodeEntry for all the new nodes in qexpand */
inline void InitNewNode(const std::vector<int> &qexpand,
const std::vector<bst_gpair> &gpair,
const RegTree &tree) {
{// setup statistics space for each tree node
for (size_t i = 0; i < stemp.size(); ++i) {
stemp[i].resize(tree.param.num_nodes, ThreadEntry());
}
snode.resize(tree.param.num_nodes, NodeEntry());
}
// setup position
const unsigned ndata = static_cast<unsigned>(position.size());
#pragma omp parallel for schedule(static)
for (unsigned i = 0; i < ndata; ++i) {
const int tid = omp_get_thread_num();
if (position[i] < 0) continue;
stemp[tid][position[i]].stats.Add(gpair[i]);
}
// sum the per thread statistics together
for (size_t j = 0; j < qexpand.size(); ++j) {
const int nid = qexpand[j];
TStats stats; stats.Clear();
for (size_t tid = 0; tid < stemp.size(); ++tid) {
stats.Add(stemp[tid][nid].stats);
}
// update node statistics
snode[nid].stats = stats;
snode[nid].root_gain = param.CalcGain(stats);
snode[nid].weight = param.CalcWeight(stats);
}
}
/*! \brief update queue expand add in new leaves */
inline void UpdateQueueExpand(const RegTree &tree, std::vector<int> *p_qexpand) {
std::vector<int> &qexpand = *p_qexpand;
std::vector<int> newnodes;
for (size_t i = 0; i < qexpand.size(); ++i) {
const int nid = qexpand[i];
if (!tree[ nid ].is_leaf()) {
newnodes.push_back(tree[nid].cleft());
newnodes.push_back(tree[nid].cright());
}
}
// use new nodes for qexpand
qexpand = newnodes;
}
// enumerate the split values of specific feature
template<typename Iter>
inline void EnumerateSplit(Iter it, unsigned fid,
const std::vector<bst_gpair> &gpair,
std::vector<ThreadEntry> &temp,
bool is_forward_search) {
// clear all the temp statistics
for (size_t j = 0; j < qexpand.size(); ++j) {
temp[qexpand[j]].stats.Clear();
}
while (it.Next()) {
const bst_uint ridx = it.rindex();
const int nid = position[ridx];
if (nid < 0) continue;
// start working
const float fvalue = it.fvalue();
// get the statistics of nid
ThreadEntry &e = temp[nid];
// test if first hit, this is fine, because we set 0 during init
if (e.stats.Empty()) {
e.stats.Add(gpair[ridx]);
e.last_fvalue = fvalue;
} else {
// try to find a split
if (fabsf(fvalue - e.last_fvalue) > rt_2eps && e.stats.sum_hess >= param.min_child_weight) {
TStats c = snode[nid].stats.Substract(e.stats);
if (c.sum_hess >= param.min_child_weight) {
double loss_chg = param.CalcGain(e.stats) + param.CalcGain(c) - snode[nid].root_gain;
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f, !is_forward_search);
}
}
// update the statistics
e.stats.Add(gpair[ridx]);
e.last_fvalue = fvalue;
}
}
// finish updating all statistics, check if it is possible to include all sum statistics
for (size_t i = 0; i < qexpand.size(); ++i) {
const int nid = qexpand[i];
ThreadEntry &e = temp[nid];
TStats c = snode[nid].stats.Substract(e.stats);
if (e.stats.sum_hess >= param.min_child_weight && c.sum_hess >= param.min_child_weight) {
const double loss_chg = param.CalcGain(e.stats) + param.CalcGain(c) - snode[nid].root_gain;
const float delta = is_forward_search ? rt_eps : -rt_eps;
e.best.Update(loss_chg, fid, e.last_fvalue + delta, !is_forward_search);
}
}
}
// find splits at current level, do split per level
inline void FindSplit(int depth, const std::vector<int> &qexpand,
const std::vector<bst_gpair> &gpair, const FMatrix &fmat,
RegTree *p_tree) {
std::vector<unsigned> feat_set = feat_index;
if (param.colsample_bylevel != 1.0f) {
random::Shuffle(feat_set);
unsigned n = static_cast<unsigned>(param.colsample_bylevel * feat_index.size());
utils::Check(n > 0, "colsample_bylevel is too small that no feature can be included");
feat_set.resize(n);
}
// start enumeration
const unsigned nsize = static_cast<unsigned>(feat_set.size());
#pragma omp parallel for schedule(dynamic, 1)
for (unsigned i = 0; i < nsize; ++i) {
const unsigned fid = feat_set[i];
const int tid = omp_get_thread_num();
if (param.need_forward_search(fmat.GetColDensity(fid))) {
this->EnumerateSplit(fmat.GetSortedCol(fid), fid, gpair, stemp[tid], true);
}
if (param.need_backward_search(fmat.GetColDensity(fid))) {
this->EnumerateSplit(fmat.GetReverseSortedCol(fid), fid, gpair, stemp[tid], false);
}
}
// after this each thread's stemp will get the best candidates, aggregate results
for (size_t i = 0; i < qexpand.size(); ++i) {
const int nid = qexpand[i];
NodeEntry &e = snode[nid];
for (int tid = 0; tid < this->nthread; ++tid) {
e.best.Update(stemp[tid][nid].best);
}
// now we know the solution in snode[nid], set split
if (e.best.loss_chg > rt_eps) {
p_tree->AddChilds(nid);
(*p_tree)[nid].set_split(e.best.split_index(), e.best.split_value, e.best.default_left());
} else {
(*p_tree)[nid].set_leaf(e.weight * param.learning_rate);
}
}
}
// reset position of each data points after split is created in the tree
inline void ResetPosition(const std::vector<int> &qexpand, const FMatrix &fmat, const RegTree &tree) {
// step 1, set default direct nodes to default, and leaf nodes to -1
const unsigned ndata = static_cast<unsigned>(position.size());
#pragma omp parallel for schedule(static)
for (unsigned i = 0; i < ndata; ++i) {
const int nid = position[i];
if (nid >= 0) {
if (tree[nid].is_leaf()) {
position[i] = -1;
} else {
// push to default branch, correct latter
position[i] = tree[nid].default_left() ? tree[nid].cleft(): tree[nid].cright();
}
}
}
// step 2, classify the non-default data into right places
std::vector<unsigned> fsplits;
for (size_t i = 0; i < qexpand.size(); ++i) {
const int nid = qexpand[i];
if (!tree[nid].is_leaf()) fsplits.push_back(tree[nid].split_index());
}
std::sort(fsplits.begin(), fsplits.end());
fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
// start put things into right place
const unsigned nfeats = static_cast<unsigned>(fsplits.size());
#pragma omp parallel for schedule(dynamic, 1)
for (unsigned i = 0; i < nfeats; ++i) {
const unsigned fid = fsplits[i];
for (typename FMatrix::ColIter it = fmat.GetSortedCol(fid); it.Next();) {
const bst_uint ridx = it.rindex();
int nid = position[ridx];
if (nid == -1) continue;
// go back to parent, correct those who are not default
nid = tree[nid].parent();
if (tree[nid].split_index() == fid) {
if (it.fvalue() < tree[nid].split_cond()) {
position[ridx] = tree[nid].cleft();
} else {
position[ridx] = tree[nid].cright();
}
}
}
}
}
//--data fields--
const TrainParam &param;
// number of omp thread used during training
int nthread;
// Per feature: shuffle index of each feature index
std::vector<unsigned> feat_index;
// Instance Data: current node position in the tree of each instance
std::vector<int> position;
// PerThread x PerTreeNode: statistics for per thread construction
std::vector< std::vector<ThreadEntry> > stemp;
/*! \brief TreeNode Data: statistics for each constructed node */
std::vector<NodeEntry> snode;
/*! \brief queue of nodes to be expanded */
std::vector<int> qexpand;
};
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_

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#ifndef XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_
#define XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_
/*!
* \file updater_prune-inl.hpp
* \brief prune a tree given the statistics
* \author Tianqi Chen
*/
#include <vector>
#include "./param.h"
#include "./updater.h"
namespace xgboost {
namespace tree {
/*! \brief pruner that prunes a tree after growing finishs */
template<typename FMatrix>
class TreePruner: public IUpdater<FMatrix> {
public:
virtual ~TreePruner(void) {}
// set training parameter
virtual void SetParam(const char *name, const char *val) {
param.SetParam(name, val);
}
// update the tree, do pruning
virtual void Update(const std::vector<bst_gpair> &gpair, FMatrix &fmat,
const std::vector<unsigned> &root_index,
const std::vector<RegTree*> &trees) {
for (size_t i = 0; i < trees.size(); ++i) {
this->DoPrune(*trees[i]);
}
}
private:
// try to prune off current leaf
inline void TryPruneLeaf(RegTree &tree, int nid, int depth) {
if (tree[nid].is_root()) return;
int pid = tree[nid].parent();
RegTree::NodeStat &s = tree.stat(pid);
++s.leaf_child_cnt;
if (s.leaf_child_cnt >= 2 && param.need_prune(s.loss_chg, depth - 1)) {
// need to be pruned
tree.ChangeToLeaf(pid, param.learning_rate * s.base_weight);
// tail recursion
this->TryPruneLeaf(tree, pid, depth - 1);
}
}
/*! \brief do prunning of a tree */
inline void DoPrune(RegTree &tree) {
// initialize auxiliary statistics
for (int nid = 0; nid < tree.param.num_nodes; ++nid) {
tree.stat(nid).leaf_child_cnt = 0;
}
for (int nid = 0; nid < tree.param.num_nodes; ++nid) {
if (tree[nid].is_leaf()) {
this->TryPruneLeaf(tree, nid, tree.GetDepth(nid));
}
}
}
private:
// training parameter
TrainParam param;
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_

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#ifndef XGBOOST_UTILS_FMAP_H_
#define XGBOOST_UTILS_FMAP_H_
/*!
* \file fmap.h
* \brief helper class that holds the feature names and interpretations
* \author Tianqi Chen
*/
#include <vector>
#include <string>
#include <cstring>
#include "./utils.h"
namespace xgboost {
namespace utils {
/*! \brief helper class that holds the feature names and interpretations */
class FeatMap {
public:
enum Type {
kIndicator = 0,
kQuantitive = 1,
kInteger = 2,
kFloat = 3
};
// function definitions
/*! \brief load feature map from text format */
inline void LoadText(const char *fname) {
FILE *fi = utils::FopenCheck(fname, "r");
this->LoadText(fi);
fclose(fi);
}
/*! \brief load feature map from text format */
inline void LoadText(FILE *fi) {
int fid;
char fname[1256], ftype[1256];
while (fscanf(fi, "%d\t%[^\t]\t%s\n", &fid, fname, ftype) == 3) {
this->PushBack(fid, fname, ftype);
}
}
/*!\brief push back feature map */
inline void PushBack(int fid, const char *fname, const char *ftype) {
utils::Check(fid == static_cast<int>(names_.size()), "invalid fmap format");
names_.push_back(std::string(fname));
types_.push_back(GetType(ftype));
}
inline void Clear(void) {
names_.clear(); types_.clear();
}
/*! \brief number of known features */
size_t size(void) const {
return names_.size();
}
/*! \brief return name of specific feature */
const char* name(size_t idx) const {
utils::Assert(idx < names_.size(), "utils::FMap::name feature index exceed bound");
return names_[idx].c_str();
}
/*! \brief return type of specific feature */
const Type& type(size_t idx) const {
utils::Assert(idx < names_.size(), "utils::FMap::name feature index exceed bound");
return types_[idx];
}
private:
inline static Type GetType(const char *tname) {
if (!strcmp("i", tname)) return kIndicator;
if (!strcmp("q", tname)) return kQuantitive;
if (!strcmp("int", tname)) return kInteger;
if (!strcmp("float", tname)) return kFloat;
utils::Error("unknown feature type, use i for indicator and q for quantity");
return kIndicator;
}
/*! \brief name of the feature */
std::vector<std::string> names_;
/*! \brief type of the feature */
std::vector<Type> types_;
};
} // namespace utils
} // namespace xgboost
#endif // XGBOOST_FMAP_H_

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#ifndef XGBOOST_UTILS_IO_H
#define XGBOOST_UTILS_IO_H
#include <cstdio>
#include <vector>
#include <string>
#include "./utils.h"
/*!
* \file io.h
* \brief general stream interface for serialization, I/O
* \author Tianqi Chen
*/
namespace xgboost {
namespace utils {
/*!
* \brief interface of stream I/O, used to serialize model
*/
class IStream {
public:
/*!
* \brief read data from stream
* \param ptr pointer to memory buffer
* \param size size of block
* \return usually is the size of data readed
*/
virtual size_t Read(void *ptr, size_t size) = 0;
/*!
* \brief write data to stream
* \param ptr pointer to memory buffer
* \param size size of block
*/
virtual void Write(const void *ptr, size_t size) = 0;
/*! \brief virtual destructor */
virtual ~IStream(void) {}
public:
// helper functions to write various of data structures
/*!
* \brief binary serialize a vector
* \param vec vector to be serialized
*/
template<typename T>
inline void Write(const std::vector<T> &vec) {
uint64_t sz = vec.size();
this->Write(&sz, sizeof(sz));
this->Write(&vec[0], sizeof(T) * sz);
}
/*!
* \brief binary load a vector
* \param out_vec vector to be loaded
* \return whether load is successfull
*/
template<typename T>
inline bool Read(std::vector<T> *out_vec) {
uint64_t sz;
if (this->Read(&sz, sizeof(sz)) == 0) return false;
out_vec->resize(sz);
if (this->Read(&(*out_vec)[0], sizeof(T) * sz) == 0) return false;
return true;
}
/*!
* \brief binary serialize a string
* \param str the string to be serialized
*/
inline void Write(const std::string &str) {
uint64_t sz = str.length();
this->Write(&sz, sizeof(sz));
this->Write(&str[0], sizeof(char) * sz);
}
/*!
* \brief binary load a string
* \param out_str string to be loaded
* \return whether load is successful
*/
inline bool Read(std::string *out_str) {
uint64_t sz;
if (this->Read(&sz, sizeof(sz)) == 0) return false;
out_str->resize(sz);
if (this->Read(&(*out_str)[0], sizeof(char) * sz) == 0) return false;
return true;
}
};
/*! \brief implementation of file i/o stream */
class FileStream : public IStream {
private:
FILE *fp;
public:
explicit FileStream(FILE *fp) {
this->fp = fp;
}
virtual size_t Read(void *ptr, size_t size) {
return fread(ptr, size, 1, fp);
}
virtual void Write(const void *ptr, size_t size) {
fwrite(ptr, size, 1, fp);
}
inline void Close(void) {
fclose(fp);
}
};
} // namespace utils
} // namespace xgboost
#endif

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#ifndef XGBOOST_UTILS_ITERATOR_H
#define XGBOOST_UTILS_ITERATOR_H
#include <cstdio>
/*!
* \file iterator.h
* \brief itertator interface
* \author Tianqi Chen
*/
namespace xgboost {
namespace utils {
/*!
* \brief iterator interface
* \tparam DType data type
*/
template<typename DType>
class IIterator {
public:
/*!
* \brief set the parameter
* \param name name of parameter
* \param val value of parameter
*/
virtual void SetParam(const char *name, const char *val) = 0;
/*! \brief initalize the iterator so that we can use the iterator */
virtual void Init(void) = 0;
/*! \brief set before first of the item */
virtual void BeforeFirst(void) = 0;
/*! \brief move to next item */
virtual bool Next(void) = 0;
/*! \brief get current data */
virtual const DType &Value(void) const = 0;
public:
/*! \brief constructor */
virtual ~IIterator(void) {}
};
} // namespace utils
} // namespace xgboost
#endif

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#ifndef XGBOOST_UTILS_MATRIX_CSR_H_
#define XGBOOST_UTILS_MATRIX_CSR_H_
/*!
* \file matrix_csr.h
* \brief this file defines some easy to use STL based class for in memory sparse CSR matrix
* \author Tianqi Chen
*/
#include <vector>
#include <algorithm>
#include "./utils.h"
namespace xgboost {
namespace utils {
/*!
* \brief a class used to help construct CSR format matrix,
* can be used to convert row major CSR to column major CSR
* \tparam IndexType type of index used to store the index position, usually unsigned or size_t
* \tparam whether enabling the usage of aclist, this option must be enabled manually
*/
template<typename IndexType, bool UseAcList = false>
struct SparseCSRMBuilder {
private:
/*! \brief dummy variable used in the indicator matrix construction */
std::vector<size_t> dummy_aclist;
/*! \brief pointer to each of the row */
std::vector<size_t> &rptr;
/*! \brief index of nonzero entries in each row */
std::vector<IndexType> &findex;
/*! \brief a list of active rows, used when many rows are empty */
std::vector<size_t> &aclist;
public:
SparseCSRMBuilder(std::vector<size_t> &p_rptr,
std::vector<IndexType> &p_findex)
:rptr(p_rptr), findex(p_findex), aclist(dummy_aclist) {
Assert(!UseAcList, "enabling bug");
}
/*! \brief use with caution! rptr must be cleaned before use */
SparseCSRMBuilder(std::vector<size_t> &p_rptr,
std::vector<IndexType> &p_findex,
std::vector<size_t> &p_aclist)
:rptr(p_rptr), findex(p_findex), aclist(p_aclist) {
Assert(UseAcList, "must manually enable the option use aclist");
}
public:
/*!
* \brief step 1: initialize the number of rows in the data, not necessary exact
* \nrows number of rows in the matrix, can be smaller than expected
*/
inline void InitBudget(size_t nrows = 0) {
if (!UseAcList) {
rptr.clear();
rptr.resize(nrows + 1, 0);
} else {
Assert(nrows + 1 == rptr.size(), "rptr must be initialized already");
this->Cleanup();
}
}
/*!
* \brief step 2: add budget to each rows, this function is called when aclist is used
* \param row_id the id of the row
* \param nelem number of element budget add to this row
*/
inline void AddBudget(size_t row_id, size_t nelem = 1) {
if (rptr.size() < row_id + 2) {
rptr.resize(row_id + 2, 0);
}
if (UseAcList) {
if (rptr[row_id + 1] == 0) aclist.push_back(row_id);
}
rptr[row_id + 1] += nelem;
}
/*! \brief step 3: initialize the necessary storage */
inline void InitStorage(void) {
// initialize rptr to be beginning of each segment
size_t start = 0;
if (!UseAcList) {
for (size_t i = 1; i < rptr.size(); i++) {
size_t rlen = rptr[i];
rptr[i] = start;
start += rlen;
}
} else {
// case with active list
std::sort(aclist.begin(), aclist.end());
for (size_t i = 0; i < aclist.size(); i++) {
size_t ridx = aclist[i];
size_t rlen = rptr[ridx + 1];
rptr[ridx + 1] = start;
// set previous rptr to right position if previous feature is not active
if (i == 0 || ridx != aclist[i - 1] + 1) rptr[ridx] = start;
start += rlen;
}
}
findex.resize(start);
}
/*!
* \brief step 4:
* used in indicator matrix construction, add new
* element to each row, the number of calls shall be exactly same as add_budget
*/
inline void PushElem(size_t row_id, IndexType col_id) {
size_t &rp = rptr[row_id + 1];
findex[rp++] = col_id;
}
/*!
* \brief step 5: only needed when aclist is used
* clean up the rptr for next usage
*/
inline void Cleanup(void) {
Assert(UseAcList, "this function can only be called use AcList");
for (size_t i = 0; i < aclist.size(); i++) {
const size_t ridx = aclist[i];
rptr[ridx] = 0; rptr[ridx + 1] = 0;
}
aclist.clear();
}
};
} // namespace utils
} // namespace xgboost
#endif

12
utils/xgboost_omp.h → utils/omp.h
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@ -1,12 +1,10 @@
#ifndef XGBOOST_OMP_H
#define XGBOOST_OMP_H
#ifndef XGBOOST_UTILS_OMP_H_
#define XGBOOST_UTILS_OMP_H_
/*!
* \file xgboost_omp.h
* \file omp.h
* \brief header to handle OpenMP compatibility issues
*
* \author Tianqi Chen: tianqi.tchen@gmail.com
* \author Tianqi Chen
*/
#if defined(_OPENMP)
#include <omp.h>
#else
@ -15,4 +13,4 @@ inline int omp_get_thread_num() { return 0; }
inline int omp_get_num_threads() { return 1; }
inline void omp_set_num_threads(int nthread) {}
#endif
#endif
#endif // XGBOOST_UTILS_OMP_H_

102
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#ifndef XGBOOST_UTILS_RANDOM_H_
#define XGBOOST_UTILS_RANDOM_H_
/*!
* \file xgboost_random.h
* \brief PRNG to support random number generation
* \author Tianqi Chen: tianqi.tchen@gmail.com
*
* Use standard PRNG from stdlib
*/
#include <cmath>
#include <cstdlib>
#include <vector>
#include <algorithm>
#include "./utils.h"
/*! namespace of PRNG */
namespace xgboost {
namespace random {
/*! \brief seed the PRNG */
inline void Seed(uint32_t seed) {
srand(seed);
}
/*! \brief return a real number uniform in [0,1) */
inline double NextDouble(void) {
return static_cast<double>(rand()) / (static_cast<double>(RAND_MAX)+1.0);
}
/*! \brief return a real numer uniform in (0,1) */
inline double NextDouble2(void) {
return (static_cast<double>(rand()) + 1.0) / (static_cast<double>(RAND_MAX)+2.0);
}
/*! \brief return a random number */
inline uint32_t NextUInt32(void) {
return (uint32_t)rand();
}
/*! \brief return a random number in n */
inline uint32_t NextUInt32(uint32_t n) {
return (uint32_t)floor(NextDouble() * n);
}
/*! \brief return x~N(0,1) */
inline double SampleNormal() {
double x, y, s;
do {
x = 2 * NextDouble2() - 1.0;
y = 2 * NextDouble2() - 1.0;
s = x*x + y*y;
} while (s >= 1.0 || s == 0.0);
return x * sqrt(-2.0 * log(s) / s);
}
/*! \brief return iid x,y ~N(0,1) */
inline void SampleNormal2D(double &xx, double &yy) {
double x, y, s;
do {
x = 2 * NextDouble2() - 1.0;
y = 2 * NextDouble2() - 1.0;
s = x*x + y*y;
} while (s >= 1.0 || s == 0.0);
double t = sqrt(-2.0 * log(s) / s);
xx = x * t;
yy = y * t;
}
/*! \brief return x~N(mu,sigma^2) */
inline double SampleNormal(double mu, double sigma) {
return SampleNormal() * sigma + mu;
}
/*! \brief return 1 with probability p, coin flip */
inline int SampleBinary(double p) {
return NextDouble() < p;
}
template<typename T>
inline void Shuffle(T *data, size_t sz) {
if (sz == 0) return;
for (uint32_t i = (uint32_t)sz - 1; i > 0; i--){
std::swap(data[i], data[NextUInt32(i + 1)]);
}
}
// random shuffle the data inside, require PRNG
template<typename T>
inline void Shuffle(std::vector<T> &data) {
Shuffle(&data[0], data.size());
}
/*! \brief random number generator with independent random number seed*/
struct Random{
/*! \brief set random number seed */
inline void Seed(unsigned sd) {
this->rseed = sd;
}
/*! \brief return a real number uniform in [0,1) */
inline double RandDouble(void) {
return static_cast<double>( rand_r( &rseed ) ) / (static_cast<double>( RAND_MAX )+1.0);
}
// random number seed
unsigned rseed;
};
} // namespace random
} // namespace xgboost
#endif // XGBOOST_UTILS_RANDOM_H_

94
utils/utils.h Normal file
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#ifndef XGBOOST_UTILS_UTILS_H_
#define XGBOOST_UTILS_UTILS_H_
/*!
* \file utils.h
* \brief simple utils to support the code
* \author Tianqi Chen
*/
#define _CRT_SECURE_NO_WARNINGS
#ifdef _MSC_VER
#define fopen64 fopen
#else
#ifdef _FILE_OFFSET_BITS
#if _FILE_OFFSET_BITS == 32
#warning "FILE OFFSET BITS defined to be 32 bit"
#endif
#endif
#ifdef __APPLE__
#define off64_t off_t
#define fopen64 fopen
#endif
#define _FILE_OFFSET_BITS 64
extern "C" {
#include <sys/types.h>
};
#endif
#ifdef _MSC_VER
typedef unsigned char uint8_t;
typedef unsigned short int uint16_t;
typedef unsigned int uint32_t;
typedef unsigned long uint64_t;
typedef long int64_t;
#else
#include <inttypes.h>
#endif
#include <cstdio>
#include <cstdarg>
#include <cstdlib>
namespace xgboost {
/*! \brief namespace for helper utils of the project */
namespace utils {
/*! \brief assert an condition is true, use this to handle debug information */
inline void Assert(bool exp, const char *fmt, ...) {
if (!exp) {
va_list args;
va_start(args, fmt);
fprintf(stderr, "AssertError:");
vfprintf(stderr, fmt, args);
va_end(args);
fprintf(stderr, "\n");
exit(-1);
}
}
/*!\brief same as assert, but this is intended to be used as message for user*/
inline void Check(bool exp, const char *fmt, ...) {
if (!exp) {
va_list args;
va_start(args, fmt);
vfprintf(stderr, fmt, args);
va_end(args);
fprintf(stderr, "\n");
exit(-1);
}
}
/*! \brief report error message, same as check */
inline void Error(const char *fmt, ...) {
{
va_list args;
va_start(args, fmt);
vfprintf(stderr, fmt, args);
va_end(args);
fprintf(stderr, "\n");
exit(-1);
}
}
/*! \brief replace fopen, report error when the file open fails */
inline FILE *FopenCheck(const char *fname, const char *flag) {
FILE *fp = fopen64(fname, flag);
Check(fp != NULL, "can not open file \"%s\"\n", fname);
return fp;
}
} // namespace utils
} // namespace xgboost
#endif // XGBOOST_UTILS_UTILS_H_

123
utils/xgboost_fmap.h
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#ifndef XGBOOST_FMAP_H
#define XGBOOST_FMAP_H
/*!
* \file xgboost_fmap.h
* \brief helper class that holds the feature names and interpretations
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <vector>
#include <string>
#include <cstring>
#include "xgboost_utils.h"
namespace xgboost{
namespace utils{
/*! \brief helper class that holds the feature names and interpretations */
class FeatMap{
public:
enum Type{
kIndicator = 0,
kQuantitive = 1,
kInteger = 2,
kFloat = 3
};
public:
/*! \brief load feature map from text format */
inline void LoadText(const char *fname){
FILE *fi = utils::FopenCheck(fname, "r");
this->LoadText(fi);
fclose(fi);
}
/*! \brief load feature map from text format */
inline void LoadText(FILE *fi){
int fid;
char fname[1256], ftype[1256];
while (fscanf(fi, "%d\t%[^\t]\t%s\n", &fid, fname, ftype) == 3){
utils::Assert(fid == (int)names_.size(), "invalid fmap format");
names_.push_back(std::string(fname));
types_.push_back(GetType(ftype));
}
}
/*! \brief number of known features */
size_t size(void) const{
return names_.size();
}
/*! \brief return name of specific feature */
const char* name(size_t idx) const{
utils::Assert(idx < names_.size(), "utils::FMap::name feature index exceed bound");
return names_[idx].c_str();
}
/*! \brief return type of specific feature */
const Type& type(size_t idx) const{
utils::Assert(idx < names_.size(), "utils::FMap::name feature index exceed bound");
return types_[idx];
}
private:
inline static Type GetType(const char *tname){
if (!strcmp("i", tname)) return kIndicator;
if (!strcmp("q", tname)) return kQuantitive;
if (!strcmp("int", tname)) return kInteger;
if (!strcmp("float", tname)) return kFloat;
utils::Error("unknown feature type, use i for indicator and q for quantity");
return kIndicator;
}
private:
/*! \brief name of the feature */
std::vector<std::string> names_;
/*! \brief type of the feature */
std::vector<Type> types_;
};
}; // namespace utils
namespace utils{
/*! \brief feature constraint, allow or disallow some feature during training */
class FeatConstrain{
public:
FeatConstrain(void){
default_state_ = +1;
}
/*!\brief set parameters */
inline void SetParam(const char *name, const char *val){
int a, b;
if (!strcmp(name, "fban")){
this->ParseRange(val, a, b);
this->SetRange(a, b, -1);
}
if (!strcmp(name, "fpass")){
this->ParseRange(val, a, b);
this->SetRange(a, b, +1);
}
if (!strcmp(name, "fdefault")){
default_state_ = atoi(val);
}
}
/*! \brief whether constrain is specified */
inline bool HasConstrain(void) const {
return state_.size() != 0 && default_state_ == 1;
}
/*! \brief whether a feature index is banned or not */
inline bool NotBanned(unsigned index) const{
int rt = index < state_.size() ? state_[index] : default_state_;
if (rt == 0) rt = default_state_;
return rt == 1;
}
private:
inline void SetRange(int a, int b, int st){
if (b >(int)state_.size()) state_.resize(b, 0);
for (int i = a; i < b; ++i){
state_[i] = st;
}
}
inline void ParseRange(const char *val, int &a, int &b){
if (sscanf(val, "%d-%d", &a, &b) == 2) return;
utils::Assert(sscanf(val, "%d", &a) == 1);
b = a + 1;
}
/*! \brief default state */
int default_state_;
/*! \brief whether the state here is, +1:pass, -1: ban, 0:default */
std::vector<int> state_;
};
}; // namespace utils
}; // namespace xgboost
#endif // XGBOOST_FMAP_H

157
utils/xgboost_matrix_csr.h
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/*!
* \file xgboost_matrix_csr.h
* \brief this file defines some easy to use STL based class for in memory sparse CSR matrix
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#ifndef XGBOOST_MATRIX_CSR_H
#define XGBOOST_MATRIX_CSR_H
#include <vector>
#include <algorithm>
#include "xgboost_utils.h"
namespace xgboost{
namespace utils{
/*!
* \brief a class used to help construct CSR format matrix,
* can be used to convert row major CSR to column major CSR
* \tparam IndexType type of index used to store the index position, usually unsigned or size_t
* \tparam whether enabling the usage of aclist, this option must be enabled manually
*/
template<typename IndexType, bool UseAcList = false>
struct SparseCSRMBuilder{
private:
/*! \brief dummy variable used in the indicator matrix construction */
std::vector<size_t> dummy_aclist;
/*! \brief pointer to each of the row */
std::vector<size_t> &rptr;
/*! \brief index of nonzero entries in each row */
std::vector<IndexType> &findex;
/*! \brief a list of active rows, used when many rows are empty */
std::vector<size_t> &aclist;
public:
SparseCSRMBuilder(std::vector<size_t> &p_rptr,
std::vector<IndexType> &p_findex)
:rptr(p_rptr), findex(p_findex), aclist(dummy_aclist){
Assert(!UseAcList, "enabling bug");
}
/*! \brief use with caution! rptr must be cleaned before use */
SparseCSRMBuilder(std::vector<size_t> &p_rptr,
std::vector<IndexType> &p_findex,
std::vector<size_t> &p_aclist)
:rptr(p_rptr), findex(p_findex), aclist(p_aclist){
Assert(UseAcList, "must manually enable the option use aclist");
}
public:
/*!
* \brief step 1: initialize the number of rows in the data, not necessary exact
* \nrows number of rows in the matrix, can be smaller than expected
*/
inline void InitBudget(size_t nrows = 0){
if (!UseAcList){
rptr.clear();
rptr.resize(nrows + 1, 0);
}
else{
Assert(nrows + 1 == rptr.size(), "rptr must be initialized already");
this->Cleanup();
}
}
/*!
* \brief step 2: add budget to each rows, this function is called when aclist is used
* \param row_id the id of the row
* \param nelem number of element budget add to this row
*/
inline void AddBudget(size_t row_id, size_t nelem = 1){
if (rptr.size() < row_id + 2){
rptr.resize(row_id + 2, 0);
}
if (UseAcList){
if (rptr[row_id + 1] == 0) aclist.push_back(row_id);
}
rptr[row_id + 1] += nelem;
}
/*! \brief step 3: initialize the necessary storage */
inline void InitStorage(void){
// initialize rptr to be beginning of each segment
size_t start = 0;
if (!UseAcList){
for (size_t i = 1; i < rptr.size(); i++){
size_t rlen = rptr[i];
rptr[i] = start;
start += rlen;
}
}
else{
// case with active list
std::sort(aclist.begin(), aclist.end());
for (size_t i = 0; i < aclist.size(); i++){
size_t ridx = aclist[i];
size_t rlen = rptr[ridx + 1];
rptr[ridx + 1] = start;
// set previous rptr to right position if previous feature is not active
if (i == 0 || ridx != aclist[i - 1] + 1) rptr[ridx] = start;
start += rlen;
}
}
findex.resize(start);
}
/*!
* \brief step 4:
* used in indicator matrix construction, add new
* element to each row, the number of calls shall be exactly same as add_budget
*/
inline void PushElem(size_t row_id, IndexType col_id){
size_t &rp = rptr[row_id + 1];
findex[rp++] = col_id;
}
/*!
* \brief step 5: only needed when aclist is used
* clean up the rptr for next usage
*/
inline void Cleanup(void){
Assert(UseAcList, "this function can only be called use AcList");
for (size_t i = 0; i < aclist.size(); i++){
const size_t ridx = aclist[i];
rptr[ridx] = 0; rptr[ridx + 1] = 0;
}
aclist.clear();
}
};
};
namespace utils{
/*!
* \brief simple sparse matrix container
* \tparam IndexType type of index used to store the index position, usually unsigned or size_t
*/
template<typename IndexType>
struct SparseCSRMat{
private:
/*! \brief pointer to each of the row */
std::vector<size_t> rptr;
/*! \brief index of nonzero entries in each row */
std::vector<IndexType> findex;
public:
/*! \brief matrix builder*/
SparseCSRMBuilder<IndexType> builder;
public:
SparseCSRMat(void) :builder(rptr, findex){
}
public:
/*! \return number of rows in the matrx */
inline size_t NumRow(void) const{
return rptr.size() - 1;
}
/*! \return number of elements r-th row */
inline size_t NumElem(size_t r) const{
return rptr[r + 1] - rptr[r];
}
/*! \return r-th row */
inline const IndexType *operator[](size_t r) const{
return &findex[rptr[r]];
}
};
};
};
#endif

148
utils/xgboost_random.h
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#ifndef XGBOOST_RANDOM_H
#define XGBOOST_RANDOM_H
/*!
* \file xgboost_random.h
* \brief PRNG to support random number generation
* \author Tianqi Chen: tianqi.tchen@gmail.com
*
* Use standard PRNG from stdlib
*/
#include <cmath>
#include <cstdlib>
#include <vector>
#ifdef _MSC_VER
typedef unsigned char uint8_t;
typedef unsigned short int uint16_t;
typedef unsigned int uint32_t;
#else
#include <inttypes.h>
#endif
/*! namespace of PRNG */
namespace xgboost{
namespace random{
/*! \brief seed the PRNG */
inline void Seed(uint32_t seed){
srand(seed);
}
/*! \brief return a real number uniform in [0,1) */
inline double NextDouble(){
return static_cast<double>(rand()) / (static_cast<double>(RAND_MAX)+1.0);
}
/*! \brief return a real numer uniform in (0,1) */
inline double NextDouble2(){
return (static_cast<double>(rand()) + 1.0) / (static_cast<double>(RAND_MAX)+2.0);
}
};
namespace random{
/*! \brief return a random number */
inline uint32_t NextUInt32(void){
return (uint32_t)rand();
}
/*! \brief return a random number in n */
inline uint32_t NextUInt32(uint32_t n){
return (uint32_t)floor(NextDouble() * n);
}
/*! \brief return x~N(0,1) */
inline double SampleNormal(){
double x, y, s;
do{
x = 2 * NextDouble2() - 1.0;
y = 2 * NextDouble2() - 1.0;
s = x*x + y*y;
} while (s >= 1.0 || s == 0.0);
return x * sqrt(-2.0 * log(s) / s);
}
/*! \brief return iid x,y ~N(0,1) */
inline void SampleNormal2D(double &xx, double &yy){
double x, y, s;
do{
x = 2 * NextDouble2() - 1.0;
y = 2 * NextDouble2() - 1.0;
s = x*x + y*y;
} while (s >= 1.0 || s == 0.0);
double t = sqrt(-2.0 * log(s) / s);
xx = x * t;
yy = y * t;
}
/*! \brief return x~N(mu,sigma^2) */
inline double SampleNormal(double mu, double sigma){
return SampleNormal() * sigma + mu;
}
/*! \brief return 1 with probability p, coin flip */
inline int SampleBinary(double p){
return NextDouble() < p;
}
/*! \brief return distribution from Gamma( alpha, beta ) */
inline double SampleGamma(double alpha, double beta) {
if (alpha < 1.0) {
double u;
do {
u = NextDouble();
} while (u == 0.0);
return SampleGamma(alpha + 1.0, beta) * pow(u, 1.0 / alpha);
}
else {
double d, c, x, v, u;
d = alpha - 1.0 / 3.0;
c = 1.0 / sqrt(9.0 * d);
do {
do {
x = SampleNormal();
v = 1.0 + c*x;
} while (v <= 0.0);
v = v * v * v;
u = NextDouble();
} while ((u >= (1.0 - 0.0331 * (x*x) * (x*x)))
&& (log(u) >= (0.5 * x * x + d * (1.0 - v + log(v)))));
return d * v / beta;
}
}
template<typename T>
inline void Exchange(T &a, T &b){
T c;
c = a;
a = b;
b = c;
}
template<typename T>
inline void Shuffle(T *data, size_t sz){
if (sz == 0) return;
for (uint32_t i = (uint32_t)sz - 1; i > 0; i--){
Exchange(data[i], data[NextUInt32(i + 1)]);
}
}
// random shuffle the data inside, require PRNG
template<typename T>
inline void Shuffle(std::vector<T> &data){
Shuffle(&data[0], data.size());
}
};
namespace random{
/*! \brief random number generator with independent random number seed*/
struct Random{
/*! \brief set random number seed */
inline void Seed( unsigned sd ){
this->rseed = sd;
}
/*! \brief return a real number uniform in [0,1) */
inline double RandDouble( void ){
return static_cast<double>( rand_r( &rseed ) ) / (static_cast<double>( RAND_MAX )+1.0);
}
// random number seed
unsigned rseed;
};
};
};
#endif

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utils/xgboost_stream.h
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#ifndef XGBOOST_STREAM_H
#define XGBOOST_STREAM_H
#include <cstdio>
/*!
* \file xgboost_stream.h
* \brief general stream interface for serialization
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
namespace xgboost{
namespace utils{
/*!
* \brief interface of stream I/O, used to serialize model
*/
class IStream{
public:
/*!
* \brief read data from stream
* \param ptr pointer to memory buffer
* \param size size of block
* \return usually is the size of data readed
*/
virtual size_t Read(void *ptr, size_t size) = 0;
/*!
* \brief write data to stream
* \param ptr pointer to memory buffer
* \param size size of block
*/
virtual void Write(const void *ptr, size_t size) = 0;
/*! \brief virtual destructor */
virtual ~IStream(void){}
};
/*! \brief implementation of file i/o stream */
class FileStream : public IStream{
private:
FILE *fp;
public:
FileStream(FILE *fp){
this->fp = fp;
}
virtual size_t Read(void *ptr, size_t size){
return fread(ptr, size, 1, fp);
}
virtual void Write(const void *ptr, size_t size){
fwrite(ptr, size, 1, fp);
}
inline void Close(void){
fclose(fp);
}
};
};
};
#endif

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utils/xgboost_utils.h
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#ifndef XGBOOST_UTILS_H
#define XGBOOST_UTILS_H
/*!
* \file xgboost_utils.h
* \brief simple utils to support the code
* \author Tianqi Chen: tianqi.tchen@gmail.com
*/
#define _CRT_SECURE_NO_WARNINGS
#ifdef _MSC_VER
#define fopen64 fopen
#else
// use 64 bit offset, either to include this header in the beginning, or
#ifdef _FILE_OFFSET_BITS
#if _FILE_OFFSET_BITS == 32
#warning "FILE OFFSET BITS defined to be 32 bit"
#endif
#endif
#ifdef __APPLE__
#define off64_t off_t
#define fopen64 fopen
#endif
#define _FILE_OFFSET_BITS 64
extern "C"{
#include <sys/types.h>
};
#include <cstdio>
#endif
#include <cstdio>
#include <cstdlib>
namespace xgboost{
/*! \brief namespace for helper utils of the project */
namespace utils{
inline void Error(const char *msg){
fprintf(stderr, "Error:%s\n", msg);
fflush(stderr);
exit(-1);
}
inline void Assert(bool exp){
if (!exp) Error("AssertError");
}
inline void Assert(bool exp, const char *msg){
if (!exp) Error(msg);
}
inline void Warning(const char *msg){
fprintf(stderr, "warning:%s\n", msg);
}
/*! \brief replace fopen, report error when the file open fails */
inline FILE *FopenCheck(const char *fname, const char *flag){
FILE *fp = fopen64(fname, flag);
if (fp == NULL){
fprintf(stderr, "can not open file \"%s\" \n", fname);
fflush(stderr);
exit(-1);
}
return fp;
}
};
};
#endif

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xgunity.cpp Normal file
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#include "tree/updater.h"
#include "gbm/gbm.h"
#include "utils/omp.h"
#include "utils/utils.h"
#include "utils/random.h"
#include "learner/objective.h"
#include "learner/learner-inl.hpp"
// pass compile flag
using namespace xgboost;
int main(void){
FMatrixS fmat(NULL);
tree::RegTree tree;
tree::TrainParam param;
std::vector<bst_gpair> gpair;
std::vector<unsigned> roots;
tree::IUpdater<FMatrixS> *up = tree::CreateUpdater<FMatrixS>("prune");
gbm::IGradBooster<FMatrixS> *gbm = new gbm::GBTree<FMatrixS>();
std::vector<tree::RegTree*> trees;
learner::IObjFunction *func = learner::CreateObjFunction("reg:linear");
learner::BoostLearner<FMatrixS> *learner= new learner::BoostLearner<FMatrixS>();
up->Update(gpair, fmat, roots, trees);
return 0;
}