add dump nice to regression demo

.gitignore

@@ -11,3 +11,9 @@
 *.lai
 *.la
 *.a
+*~
+*txt*
+*conf
+*buffer
+*model
+xgboost

LICENSE

@@ -1,202 +1,13 @@
-Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
+Copyright (c) 2014 Tianqi Chen
 
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+Unless required by applicable law or agreed to in writing, software
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Makefile

@@ -0,0 +1,25 @@
+export CC  = gcc
+export CXX = g++
+export CFLAGS = -Wall -O3 -msse2  -Wno-unknown-pragmas -fopenmp
+
+# specify tensor path
+BIN = xgboost
+OBJ = 
+.PHONY: clean all
+
+all: $(BIN) $(OBJ)
+export LDFLAGS= -pthread -lm 
+
+xgboost: regression/xgboost_reg_main.cpp regression/*.h booster/*.h booster/*/*.hpp booster/*.hpp
+
+$(BIN) : 
+	$(CXX) $(CFLAGS) $(LDFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)
+
+$(OBJ) : 
+	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) )
+
+install:
+	cp -f -r $(BIN)  $(INSTALL_PATH)
+
+clean:
+	$(RM) $(OBJ) $(BIN) *~

README.md

@@ -1,4 +1,40 @@
-xgboost
+xgboost: eXtreme Gradient Boosting 
 =======
+A General purpose gradient boosting (tree) library.
 
-General Purpose Gradient Boosting Library
+Authors:
+* Tianqi Chen, project creater
+* Kailong Chen, contributes regression module
+
+Turorial and Documentation: https://github.com/tqchen/xgboost/wiki
+ 
+Features
+=======
+* 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.
+* Layout of gradient boosting algorithm to support generic tasks, see project wiki.
+
+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
+
+File extension convention
+=======
+* .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

booster/linear/xgboost_linear.hpp

@@ -0,0 +1,200 @@
+#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

booster/tree/xgboost_base_treemaker.hpp

@@ -0,0 +1,147 @@
+#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

booster/tree/xgboost_col_treemaker.hpp

@@ -0,0 +1,335 @@
+#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

booster/tree/xgboost_row_treemaker.hpp

@@ -0,0 +1,386 @@
+#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

booster/tree/xgboost_svdf_tree.hpp

@@ -0,0 +1,430 @@
+#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;
+            const TreeParamTrain &param;
+        public:
+            RTSelecter( const TreeParamTrain &p ):param( p ){
+                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( param );
+                
+                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( param );
+                // 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
+
+

booster/tree/xgboost_tree.hpp

@@ -0,0 +1,268 @@
+#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

booster/tree/xgboost_tree_model.h

@@ -0,0 +1,554 @@
+#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

booster/xgboost-inl.hpp

@@ -0,0 +1,39 @@
+#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

booster/xgboost.h

@@ -0,0 +1,157 @@
+#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

booster/xgboost_data.h

@@ -0,0 +1,393 @@
+#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() );
+                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_ );
+                }
+            }
+            /*!
+            * \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) ), "Load FMatrixS" );
+
+                data.resize( ptr.back() ); 
+                if( data.size() != 0 ){
+                    utils::Assert( fi.Read( &data[0] , data.size() * sizeof(REntry) ) , "Load FMatrixS" );
+                }
+            }
+        protected:
+            /*! \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

booster/xgboost_gbmbase.h

@@ -0,0 +1,385 @@
+#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.num_pbuffer );
+                    pred_counter.resize( mparam.num_pbuffer );
+                    utils::Assert( fi.Read( &pred_buffer[0] , pred_buffer.size()*sizeof(float) ) != 0 );
+                    utils::Assert( fi.Read( &pred_counter[0], pred_counter.size()*sizeof(unsigned) ) != 0 );
+                }
+            }
+            /*! 
+             * \brief save model to stream
+             * \param fo output stream
+             */
+            inline void SaveModel( utils::IStream &fo ) const {
+                utils::Assert( mparam.num_boosters == (int)boosters.size() );
+                fo.Write( &mparam, sizeof(ModelParam) );
+                for( size_t i = 0; i < boosters.size(); i ++ ){
+                    boosters[ i ]->SaveModel( fo ); 
+                }
+                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.num_pbuffer, 0.0 );
+                pred_counter.resize( mparam.num_pbuffer, 0 );
+                utils::Assert( mparam.num_boosters == 0 );
+                utils::Assert( boosters.size() == 0 );
+            }
+            /*!
+             * \brief initialize solver before training, called before training
+             * this function is reserved for solver to allocate necessary space and do other preparation 
+             */            
+            inline void InitTrainer( void ){
+                if( tparam.nthread != 0 ){
+                    omp_set_num_threads( tparam.nthread );
+                }
+                // 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
+             */
+            inline void DoBoost( std::vector<float> &grad,
+                                 std::vector<float> &hess,
+                                 const booster::FMatrixS &feats,
+                                 const std::vector<unsigned> &root_index ) {
+                booster::IBooster *bst = this->GetUpdateBooster();
+                bst->DoBoost( grad, hess, feats, root_index );
+            }
+            /*! 
+             * \brief predict values for given sparse feature vector
+             *   NOTE: in tree implementation, this is only OpenMP threadsafe, but not threadsafe
+             * \param feats feature matrix
+             * \param row_index  row index in the feature matrix
+             * \param buffer_index the buffer index of the current feature line, default -1 means no buffer assigned
+             * \param root_index root id of current instance, default = 0
+             * \return prediction 
+             */
+            inline float Predict( const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0 ){
+                size_t istart = 0;
+                float  psum = 0.0f;
+
+                // load buffered results if any
+                if( mparam.do_reboost == 0 && buffer_index >= 0 ){
+                    utils::Assert( buffer_index < mparam.num_pbuffer, "buffer index exceed num_pbuffer" );
+                    istart = this->pred_counter[ buffer_index ];
+                    psum   = this->pred_buffer [ buffer_index ];
+                }
+            
+                for( size_t i = istart; i < this->boosters.size(); i ++ ){
+                    psum += this->boosters[ i ]->Predict( feats, row_index, root_index );
+                }                
+                // updated the buffered results
+                if( mparam.do_reboost == 0 && buffer_index >= 0 ){
+                    this->pred_counter[ buffer_index ] = static_cast<unsigned>( boosters.size() );
+                    this->pred_buffer [ buffer_index ] = psum;
+                }
+                return psum;
+            }
+        public:
+            //--------trial code for interactive update an existing booster------
+            //-------- usually not needed, ignore this region ---------
+            /*! 
+             * \brief same as Predict, but removes the prediction of booster to be updated 
+             *        this function must be called once and only once for every data with pbuffer
+             */
+            inline float InteractPredict( const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0 ){
+                float psum = this->Predict( feats, row_index, buffer_index, root_index );
+                if( tparam.reupdate_booster != -1 ){
+                    const int bid = tparam.reupdate_booster;
+                    utils::Assert( bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound" );
+                    psum -= boosters[ bid ]->Predict( feats, row_index, root_index );
+                    if( mparam.do_reboost == 0 && buffer_index >= 0 ){
+                        this->pred_buffer[ buffer_index ] = psum;
+                    }
+                }
+                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() );
+            }
+            /*! \brief update the prediction buffer, after booster have been updated */            
+            inline void InteractRePredict( const FMatrixS &feats, bst_uint row_index, int buffer_index = -1, unsigned root_index = 0 ){
+                if( tparam.reupdate_booster != -1 ){
+                    const int bid = tparam.reupdate_booster;
+                    utils::Assert( bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound" );
+                    if( mparam.do_reboost == 0 && buffer_index >= 0 ){
+                        this->pred_buffer[ buffer_index ] += boosters[ bid ]->Predict( feats, row_index, root_index );
+                    }
+                }
+            }
+            //-----------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( void ){
+                if( tparam.reupdate_booster != -1 ){
+                    const int bid = tparam.reupdate_booster;
+                    utils::Assert( bid >= 0 && bid < (int)boosters.size(), "interact:booster_index exceed existing bound" );
+                    this->ConfigBooster( boosters[bid] );
+                    return boosters[ bid ];
+                }
+
+                if( mparam.do_reboost == 0 || boosters.size() == 0 ){
+                    mparam.num_boosters += 1;
+                    boosters.push_back( booster::CreateBooster<FMatrixS>( mparam.booster_type ) );
+                    booster_info.push_back( 0 );
+                    this->ConfigBooster( boosters.back() );
+                    boosters.back()->InitModel();                    
+                }else{
+                    this->ConfigBooster( boosters.back() );
+                }
+                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 reserved parameters */
+                int reserved[ 32 ];
+                /*! \brief constructor */
+                ModelParam( void ){
+                    num_boosters = 0; 
+                    booster_type = 0;
+                    num_roots = num_feature = 0;                    
+                    do_reboost = 0;
+                    num_pbuffer = 0;
+                    memset( reserved, 0, sizeof( reserved ) );
+                }
+                /*! 
+                 * \brief set parameters from outside 
+                 * \param name name of the parameter
+                 * \param val  value of the parameter
+                 */
+                inline void SetParam( const char *name, const char *val ){
+                    if( !strcmp("booster_type", name ) ){
+                        booster_type = atoi( val );
+                        // 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("bst:num_roots", name ) )    num_roots = atoi( val );
+                    if( !strcmp("bst:num_feature", name ) )  num_feature = atoi( val );
+                }
+            };
+            /*! \brief training parameters */
+            struct TrainParam{
+                /*! \brief number of OpenMP threads */
+                int nthread;
+                /*! 
+                 * \brief index of specific booster to be re-updated, default = -1: update new booster 
+                 *  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

demo/binary_classification/README

@@ -0,0 +1,14 @@
+Demonstrating how to use XGBoost accomplish binary classification tasks  on UCI mushroom dataset  http://archive.ics.uci.edu/ml/datasets/Mushroom
+
+Run: ./runexp.sh
+
+Format of input: LIBSVM format
+
+Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
+  - Feature id must be from 0 to number of features, in sorted order.
+  - i means this feature is binary indicator feature
+  - q means this feature is a quantitative value, such as age, time, can be missing
+  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)
+
+
+Explainations: https://github.com/tqchen/xgboost/wiki/Binary-Classification

demo/binary_classification/agaricus-lepiota.fmap

@@ -0,0 +1,32 @@
+     1. cap-shape:                bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s
+     2. cap-surface:              fibrous=f,grooves=g,scaly=y,smooth=s
+     3. cap-color:                brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y
+     4. bruises?:                 bruises=t,no=f
+     5. odor:                     almond=a,anise=l,creosote=c,fishy=y,foul=f,
+                                  musty=m,none=n,pungent=p,spicy=s
+     6. gill-attachment:          attached=a,descending=d,free=f,notched=n
+     7. gill-spacing:             close=c,crowded=w,distant=d
+     8. gill-size:                broad=b,narrow=n
+     9. gill-color:               black=k,brown=n,buff=b,chocolate=h,gray=g,
+                                  green=r,orange=o,pink=p,purple=u,red=e,
+                                  white=w,yellow=y
+    10. stalk-shape:              enlarging=e,tapering=t
+    11. stalk-root:               bulbous=b,club=c,cup=u,equal=e,
+                                  rhizomorphs=z,rooted=r,missing=?
+    12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
+    13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
+    14. stalk-color-above-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
+                                  pink=p,red=e,white=w,yellow=y
+    15. stalk-color-below-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
+                                  pink=p,red=e,white=w,yellow=y
+    16. veil-type:                partial=p,universal=u
+    17. veil-color:               brown=n,orange=o,white=w,yellow=y
+    18. ring-number:              none=n,one=o,two=t
+    19. ring-type:                cobwebby=c,evanescent=e,flaring=f,large=l,
+                                  none=n,pendant=p,sheathing=s,zone=z
+    20. spore-print-color:        black=k,brown=n,buff=b,chocolate=h,green=r,
+                                  orange=o,purple=u,white=w,yellow=y
+    21. population:               abundant=a,clustered=c,numerous=n,
+                                  scattered=s,several=v,solitary=y
+    22. habitat:                  grasses=g,leaves=l,meadows=m,paths=p,
+                                  urban=u,waste=w,woods=d

demo/binary_classification/agaricus-lepiota.names

@@ -0,0 +1,148 @@
+1. Title: Mushroom Database
+
+2. Sources: 
+    (a) Mushroom records drawn from The Audubon Society Field Guide to North
+        American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred
+        A. Knopf
+    (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
+    (c) Date: 27 April 1987
+
+3. Past Usage:
+    1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational
+       Adjustment (Technical Report 87-19).  Doctoral disseration, Department
+       of Information and Computer Science, University of California, Irvine.
+       --- STAGGER: asymptoted to 95% classification accuracy after reviewing
+           1000 instances.
+    2. Iba,W., Wogulis,J., & Langley,P. (1988).  Trading off Simplicity
+       and Coverage in Incremental Concept Learning. In Proceedings of 
+       the 5th International Conference on Machine Learning, 73-79.
+       Ann Arbor, Michigan: Morgan Kaufmann.  
+       -- approximately the same results with their HILLARY algorithm    
+    3. In the following references a set of rules (given below) were
+	learned for this data set which may serve as a point of
+	comparison for other researchers.
+
+	Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules
+	from training data using backpropagation networks, in: Proc. of the
+	The 1st Online Workshop on Soft Computing, 19-30.Aug.1996, pp. 25-30,
+	available on-line at: http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
+
+	Duch W, Adamczak R, Grabczewski K, Ishikawa M, Ueda H, Extraction of
+	crisp logical rules using constrained backpropagation networks -
+	comparison of two new approaches, in: Proc. of the European Symposium
+	on Artificial Neural Networks (ESANN'97), Bruge, Belgium 16-18.4.1997,
+	pp. xx-xx
+
+	Wlodzislaw Duch, Department of Computer Methods, Nicholas Copernicus
+	University, 87-100 Torun, Grudziadzka 5, Poland
+	e-mail: duch@phys.uni.torun.pl
+	WWW     http://www.phys.uni.torun.pl/kmk/
+	
+	Date: Mon, 17 Feb 1997 13:47:40 +0100
+	From: Wlodzislaw Duch <duch@phys.uni.torun.pl>
+	Organization: Dept. of Computer Methods, UMK
+
+	I have attached a file containing logical rules for mushrooms.
+	It should be helpful for other people since only in the last year I
+	have seen about 10 papers analyzing this dataset and obtaining quite
+	complex rules. We will try to contribute other results later.
+
+	With best regards, Wlodek Duch
+	________________________________________________________________
+
+	Logical rules for the mushroom data sets.
+
+	Logical rules given below seem to be the simplest possible for the
+	mushroom dataset and therefore should be treated as benchmark results.
+
+	Disjunctive rules for poisonous mushrooms, from most general
+	to most specific:
+
+	P_1) odor=NOT(almond.OR.anise.OR.none)
+	     120 poisonous cases missed, 98.52% accuracy
+
+	P_2) spore-print-color=green
+	     48 cases missed, 99.41% accuracy
+         
+	P_3) odor=none.AND.stalk-surface-below-ring=scaly.AND.
+	          (stalk-color-above-ring=NOT.brown) 
+	     8 cases missed, 99.90% accuracy
+         
+	P_4) habitat=leaves.AND.cap-color=white
+	         100% accuracy     
+
+	Rule P_4) may also be
+
+	P_4') population=clustered.AND.cap_color=white
+
+	These rule involve 6 attributes (out of 22). Rules for edible
+	mushrooms are obtained as negation of the rules given above, for
+	example the rule:
+
+	odor=(almond.OR.anise.OR.none).AND.spore-print-color=NOT.green
+
+	gives 48 errors, or 99.41% accuracy on the whole dataset.
+
+	Several slightly more complex variations on these rules exist,
+	involving other attributes, such as gill_size, gill_spacing,
+	stalk_surface_above_ring, but the rules given above are the simplest
+	we have found.
+
+
+4. Relevant Information:
+    This data set includes descriptions of hypothetical samples
+    corresponding to 23 species of gilled mushrooms in the Agaricus and
+    Lepiota Family (pp. 500-525).  Each species is identified as
+    definitely edible, definitely poisonous, or of unknown edibility and
+    not recommended.  This latter class was combined with the poisonous
+    one.  The Guide clearly states that there is no simple rule for
+    determining the edibility of a mushroom; no rule like ``leaflets
+    three, let it be'' for Poisonous Oak and Ivy.
+
+5. Number of Instances: 8124
+
+6. Number of Attributes: 22 (all nominally valued)
+
+7. Attribute Information: (classes: edible=e, poisonous=p)
+     1. cap-shape:                bell=b,conical=c,convex=x,flat=f,
+                                  knobbed=k,sunken=s
+     2. cap-surface:              fibrous=f,grooves=g,scaly=y,smooth=s
+     3. cap-color:                brown=n,buff=b,cinnamon=c,gray=g,green=r,
+                                  pink=p,purple=u,red=e,white=w,yellow=y
+     4. bruises?:                 bruises=t,no=f
+     5. odor:                     almond=a,anise=l,creosote=c,fishy=y,foul=f,
+                                  musty=m,none=n,pungent=p,spicy=s
+     6. gill-attachment:          attached=a,descending=d,free=f,notched=n
+     7. gill-spacing:             close=c,crowded=w,distant=d
+     8. gill-size:                broad=b,narrow=n
+     9. gill-color:               black=k,brown=n,buff=b,chocolate=h,gray=g,
+                                  green=r,orange=o,pink=p,purple=u,red=e,
+                                  white=w,yellow=y
+    10. stalk-shape:              enlarging=e,tapering=t
+    11. stalk-root:               bulbous=b,club=c,cup=u,equal=e,
+                                  rhizomorphs=z,rooted=r,missing=?
+    12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
+    13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
+    14. stalk-color-above-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
+                                  pink=p,red=e,white=w,yellow=y
+    15. stalk-color-below-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
+                                  pink=p,red=e,white=w,yellow=y
+    16. veil-type:                partial=p,universal=u
+    17. veil-color:               brown=n,orange=o,white=w,yellow=y
+    18. ring-number:              none=n,one=o,two=t
+    19. ring-type:                cobwebby=c,evanescent=e,flaring=f,large=l,
+                                  none=n,pendant=p,sheathing=s,zone=z
+    20. spore-print-color:        black=k,brown=n,buff=b,chocolate=h,green=r,
+                                  orange=o,purple=u,white=w,yellow=y
+    21. population:               abundant=a,clustered=c,numerous=n,
+                                  scattered=s,several=v,solitary=y
+    22. habitat:                  grasses=g,leaves=l,meadows=m,paths=p,
+                                  urban=u,waste=w,woods=d
+
+8. Missing Attribute Values: 2480 of them (denoted by "?"), all for
+   attribute #11.
+
+9. Class Distribution: 
+    --    edible: 4208 (51.8%)
+    -- poisonous: 3916 (48.2%)
+    --     total: 8124 instances

demo/binary_classification/mapfeat.py

@@ -0,0 +1,50 @@
+#!/usr/bin/python
+import sys
+
+def loadfmap( fname ):
+    fmap = {}
+    nmap = {}
+    
+    for l in open( fname ):
+        arr = l.split()
+        if arr[0].find('.') != -1:            
+            idx = int( arr[0].strip('.') )
+            assert idx not in fmap        
+            fmap[ idx ] = {}
+            ftype = arr[1].strip(':')        
+            content = arr[2]
+        else:
+            content = arr[0]
+        for it in content.split(','):
+            if it.strip() == '':
+                continue
+            k , v = it.split('=')
+            fmap[ idx ][ v ] = len(nmap)
+            nmap[ len(nmap) ] = ftype+'='+k
+    return fmap, nmap
+
+def write_nmap( fo, nmap ):    
+    for i in xrange( len(nmap) ):
+        fo.write('%d\t%s\ti\n' % (i, nmap[i]) )
+
+# start here
+fmap, nmap = loadfmap( 'agaricus-lepiota.fmap' )
+fo = open( 'featmap.txt', 'w' )
+write_nmap( fo, nmap )
+fo.close()
+
+fo = open( 'agaricus.txt', 'w' ) 
+for l in open( 'agaricus-lepiota.data' ):
+    arr = l.split(',')
+    if arr[0] == 'p':
+        fo.write('1')
+    else:
+        assert arr[0] == 'e'
+        fo.write('0')
+    for i in xrange( 1,len(arr) ):
+        fo.write( ' %d:1' % fmap[i][arr[i].strip()] )
+    fo.write('\n')
+
+fo.close()
+
+ 

demo/binary_classification/mknfold.py

@@ -0,0 +1,29 @@
+#!/usr/bin/python
+import sys
+import random
+
+if len(sys.argv) < 2:
+    print 'Usage:<filename> <k> [nfold = 5]'
+    exit(0)
+
+random.seed( 10 )
+
+k = int( sys.argv[2] )
+if len(sys.argv) > 3:
+    nfold = int( sys.argv[3] )
+else:
+    nfold = 5
+
+fi = open( sys.argv[1], 'r' )
+ftr = open( sys.argv[1]+'.train', 'w' )
+fte = open( sys.argv[1]+'.test', 'w' )
+for l in fi:
+    if random.randint( 1 , nfold ) == k:
+        fte.write( l )
+    else:
+        ftr.write( l )
+
+fi.close()
+ftr.close()
+fte.close()
+

demo/binary_classification/mushroom.conf

@@ -0,0 +1,27 @@
+# General Parameters, see comment for each definition
+# choose the tree booster, 0: tree, 1: linear
+booster_type = 0 
+# choose logistic regression loss function for binary classification
+loss_type = 2 
+
+# Tree Booster Parameters
+# step size shrinkage
+bst:eta = 1.0 
+# minimum loss reduction required to make a further partition
+bst:gamma = 1.0 
+# minimum sum of instance weight(hessian) needed in a child
+bst:min_child_weight = 1 
+# maximum depth of a tree
+bst:max_depth = 3 
+
+# Task Parameters
+# the number of round to do boosting
+num_round = 2
+# 0 means do not save any model except the final round model
+save_period = 0 
+# The path of training data
+data = "agaricus.txt.train" 
+# The path of validation data, used to monitor training process, here [test] sets name of the validation set
+eval[test] = "agaricus.txt.test" 
+# The path of test data 
+test:data = "agaricus.txt.test"      

demo/binary_classification/runexp.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+# map feature using indicator encoding, also produce featmap.txt
+python mapfeat.py
+# split train and test
+python mknfold.py agaricus.txt 1
+# training and output the models
+../../xgboost mushroom.conf
+# output prediction task=pred 
+../../xgboost mushroom.conf task=pred model_in=0002.model
+# print the boosters of 00002.model in dump.raw.txt
+../../xgboost mushroom.conf task=dump model_in=0002.model name_dump=dump.raw.txt 
+# use the feature map in printing for better visualization
+../../xgboost mushroom.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt
+cat dump.nice.txt
+

demo/regression/README

@@ -0,0 +1,13 @@
+Demonstrating how to use XGBoost accomplish regression tasks on computer hardware dataset https://archive.ics.uci.edu/ml/datasets/Computer+Hardware
+
+Run: ./runexp.sh
+
+Format of input: LIBSVM format
+
+Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
+  - Feature id must be from 0 to number of features, in sorted order.
+  - i means this feature is binary indicator feature
+  - q means this feature is a quantitative value, such as age, time, can be missing
+  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)
+
+Explainations: https://github.com/tqchen/xgboost/wiki/Regression

demo/regression/machine.conf

@@ -0,0 +1,30 @@
+# General Parameters, see comment for each definition
+# choose the tree booster, 0: tree, 1: linear
+booster_type = 0 
+# this is the only difference with classification, use 0: linear regression
+# when labels are in [0,1] we can also use 1: logistic regression
+loss_type = 0
+
+# Tree Booster Parameters
+# step size shrinkage
+bst:eta = 1.0 
+# minimum loss reduction required to make a further partition
+bst:gamma = 1.0 
+# minimum sum of instance weight(hessian) needed in a child
+bst:min_child_weight = 1 
+# maximum depth of a tree
+bst:max_depth = 3 
+
+# Task parameters
+# the number of round to do boosting
+num_round = 2
+# 0 means do not save any model except the final round model
+save_period = 0 
+# The path of training data
+data = "machine.txt.train" 
+# The path of validation data, used to monitor training process, here [test] sets name of the validation set
+eval[test] = "machine.txt.test" 
+# The path of test data 
+test:data = "machine.txt.test"      
+
+

demo/regression/machine.data

@@ -0,0 +1,209 @@
+adviser,32/60,125,256,6000,256,16,128,198,199
+amdahl,470v/7,29,8000,32000,32,8,32,269,253
+amdahl,470v/7a,29,8000,32000,32,8,32,220,253
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+amdahl,470v/7c,29,8000,16000,32,8,16,132,132
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+amdahl,580-5880,23,32000,64000,128,32,64,1144,1238
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+cdc,cyber:170/845,64,5240,20970,30,12,24,208,136
+cdc,omega:480-i,50,500,2000,8,1,4,20,23
+cdc,omega:480-ii,50,1000,4000,8,1,5,29,29
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+dec,decsys:20:2060,133,1000,8000,9,3,12,72,41
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+dg,eclipse:mv/6000,110,1000,4000,16,1,2,26,30
+dg,eclipse:mv/8000,110,1000,12000,16,1,2,60,56
+dg,eclipse:mv/8000-ii,220,1000,8000,16,1,2,71,42
+formation,f4000/100,800,256,8000,0,1,4,12,34
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+formation,f4000/300,800,256,8000,0,1,4,16,34
+formation,f4000/300ap,800,256,8000,0,1,4,22,34
+four-phase,2000/260,125,512,1000,0,8,20,36,19
+gould,concept:32/8705,75,2000,8000,64,1,38,144,75
+gould,concept:32/8750,75,2000,16000,64,1,38,144,113
+gould,concept:32/8780,75,2000,16000,128,1,38,259,157
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+ibm,370/125-2,480,96,512,0,1,1,6,15
+ibm,370/148,203,1000,2000,0,1,5,24,21
+ibm,370/158-3,115,512,6000,16,1,6,45,35
+ibm,38/3,1100,512,1500,0,1,1,7,18
+ibm,38/4,1100,768,2000,0,1,1,13,20
+ibm,38/5,600,768,2000,0,1,1,16,20
+ibm,38/7,400,2000,4000,0,1,1,32,28
+ibm,38/8,400,4000,8000,0,1,1,32,45
+ibm,4321,900,1000,1000,0,1,2,11,18
+ibm,4331-1,900,512,1000,0,1,2,11,17
+ibm,4331-11,900,1000,4000,4,1,2,18,26
+ibm,4331-2,900,1000,4000,8,1,2,22,28
+ibm,4341,900,2000,4000,0,3,6,37,28
+ibm,4341-1,225,2000,4000,8,3,6,40,31
+ibm,4341-10,225,2000,4000,8,3,6,34,31
+ibm,4341-11,180,2000,8000,8,1,6,50,42
+ibm,4341-12,185,2000,16000,16,1,6,76,76
+ibm,4341-2,180,2000,16000,16,1,6,66,76
+ibm,4341-9,225,1000,4000,2,3,6,24,26
+ibm,4361-4,25,2000,12000,8,1,4,49,59
+ibm,4361-5,25,2000,12000,16,3,5,66,65
+ibm,4381-1,17,4000,16000,8,6,12,100,101
+ibm,4381-2,17,4000,16000,32,6,12,133,116
+ibm,8130-a,1500,768,1000,0,0,0,12,18
+ibm,8130-b,1500,768,2000,0,0,0,18,20
+ibm,8140,800,768,2000,0,0,0,20,20
+ipl,4436,50,2000,4000,0,3,6,27,30
+ipl,4443,50,2000,8000,8,3,6,45,44
+ipl,4445,50,2000,8000,8,1,6,56,44
+ipl,4446,50,2000,16000,24,1,6,70,82
+ipl,4460,50,2000,16000,24,1,6,80,82
+ipl,4480,50,8000,16000,48,1,10,136,128
+magnuson,m80/30,100,1000,8000,0,2,6,16,37
+magnuson,m80/31,100,1000,8000,24,2,6,26,46
+magnuson,m80/32,100,1000,8000,24,3,6,32,46
+magnuson,m80/42,50,2000,16000,12,3,16,45,80
+magnuson,m80/43,50,2000,16000,24,6,16,54,88
+magnuson,m80/44,50,2000,16000,24,6,16,65,88
+microdata,seq.ms/3200,150,512,4000,0,8,128,30,33
+nas,as/3000,115,2000,8000,16,1,3,50,46
+nas,as/3000-n,115,2000,4000,2,1,5,40,29
+nas,as/5000,92,2000,8000,32,1,6,62,53
+nas,as/5000-e,92,2000,8000,32,1,6,60,53
+nas,as/5000-n,92,2000,8000,4,1,6,50,41
+nas,as/6130,75,4000,16000,16,1,6,66,86
+nas,as/6150,60,4000,16000,32,1,6,86,95
+nas,as/6620,60,2000,16000,64,5,8,74,107
+nas,as/6630,60,4000,16000,64,5,8,93,117
+nas,as/6650,50,4000,16000,64,5,10,111,119
+nas,as/7000,72,4000,16000,64,8,16,143,120
+nas,as/7000-n,72,2000,8000,16,6,8,105,48
+nas,as/8040,40,8000,16000,32,8,16,214,126
+nas,as/8050,40,8000,32000,64,8,24,277,266
+nas,as/8060,35,8000,32000,64,8,24,370,270
+nas,as/9000-dpc,38,16000,32000,128,16,32,510,426
+nas,as/9000-n,48,4000,24000,32,8,24,214,151
+nas,as/9040,38,8000,32000,64,8,24,326,267
+nas,as/9060,30,16000,32000,256,16,24,510,603
+ncr,v8535:ii,112,1000,1000,0,1,4,8,19
+ncr,v8545:ii,84,1000,2000,0,1,6,12,21
+ncr,v8555:ii,56,1000,4000,0,1,6,17,26
+ncr,v8565:ii,56,2000,6000,0,1,8,21,35
+ncr,v8565:ii-e,56,2000,8000,0,1,8,24,41
+ncr,v8575:ii,56,4000,8000,0,1,8,34,47
+ncr,v8585:ii,56,4000,12000,0,1,8,42,62
+ncr,v8595:ii,56,4000,16000,0,1,8,46,78
+ncr,v8635,38,4000,8000,32,16,32,51,80
+ncr,v8650,38,4000,8000,32,16,32,116,80
+ncr,v8655,38,8000,16000,64,4,8,100,142
+ncr,v8665,38,8000,24000,160,4,8,140,281
+ncr,v8670,38,4000,16000,128,16,32,212,190
+nixdorf,8890/30,200,1000,2000,0,1,2,25,21
+nixdorf,8890/50,200,1000,4000,0,1,4,30,25
+nixdorf,8890/70,200,2000,8000,64,1,5,41,67
+perkin-elmer,3205,250,512,4000,0,1,7,25,24
+perkin-elmer,3210,250,512,4000,0,4,7,50,24
+perkin-elmer,3230,250,1000,16000,1,1,8,50,64
+prime,50-2250,160,512,4000,2,1,5,30,25
+prime,50-250-ii,160,512,2000,2,3,8,32,20
+prime,50-550-ii,160,1000,4000,8,1,14,38,29
+prime,50-750-ii,160,1000,8000,16,1,14,60,43
+prime,50-850-ii,160,2000,8000,32,1,13,109,53
+siemens,7.521,240,512,1000,8,1,3,6,19
+siemens,7.531,240,512,2000,8,1,5,11,22
+siemens,7.536,105,2000,4000,8,3,8,22,31
+siemens,7.541,105,2000,6000,16,6,16,33,41
+siemens,7.551,105,2000,8000,16,4,14,58,47
+siemens,7.561,52,4000,16000,32,4,12,130,99
+siemens,7.865-2,70,4000,12000,8,6,8,75,67
+siemens,7.870-2,59,4000,12000,32,6,12,113,81
+siemens,7.872-2,59,8000,16000,64,12,24,188,149
+siemens,7.875-2,26,8000,24000,32,8,16,173,183
+siemens,7.880-2,26,8000,32000,64,12,16,248,275
+siemens,7.881-2,26,8000,32000,128,24,32,405,382
+sperry,1100/61-h1,116,2000,8000,32,5,28,70,56
+sperry,1100/81,50,2000,32000,24,6,26,114,182
+sperry,1100/82,50,2000,32000,48,26,52,208,227
+sperry,1100/83,50,2000,32000,112,52,104,307,341
+sperry,1100/84,50,4000,32000,112,52,104,397,360
+sperry,1100/93,30,8000,64000,96,12,176,915,919
+sperry,1100/94,30,8000,64000,128,12,176,1150,978
+sperry,80/3,180,262,4000,0,1,3,12,24
+sperry,80/4,180,512,4000,0,1,3,14,24
+sperry,80/5,180,262,4000,0,1,3,18,24
+sperry,80/6,180,512,4000,0,1,3,21,24
+sperry,80/8,124,1000,8000,0,1,8,42,37
+sperry,90/80-model-3,98,1000,8000,32,2,8,46,50
+sratus,32,125,2000,8000,0,2,14,52,41
+wang,vs-100,480,512,8000,32,0,0,67,47
+wang,vs-90,480,1000,4000,0,0,0,45,25

demo/regression/machine.names

@@ -0,0 +1,72 @@
+1. Title: Relative CPU Performance Data 
+
+2. Source Information
+   -- Creators: Phillip Ein-Dor and Jacob Feldmesser
+     -- Ein-Dor: Faculty of Management; Tel Aviv University; Ramat-Aviv; 
+        Tel Aviv, 69978; Israel
+   -- Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779   
+   -- Date: October, 1987
+ 
+3. Past Usage:
+    1. Ein-Dor and Feldmesser (CACM 4/87, pp 308-317)
+       -- Results: 
+          -- linear regression prediction of relative cpu performance
+          -- Recorded 34% average deviation from actual values 
+    2. Kibler,D. & Aha,D. (1988).  Instance-Based Prediction of
+       Real-Valued Attributes.  In Proceedings of the CSCSI (Canadian
+       AI) Conference.
+       -- Results:
+          -- instance-based prediction of relative cpu performance
+          -- similar results; no transformations required
+    - Predicted attribute: cpu relative performance (numeric)
+
+4. Relevant Information:
+   -- The estimated relative performance values were estimated by the authors
+      using a linear regression method.  See their article (pp 308-313) for
+      more details on how the relative performance values were set.
+
+5. Number of Instances: 209 
+
+6. Number of Attributes: 10 (6 predictive attributes, 2 non-predictive, 
+                             1 goal field, and the linear regression's guess)
+
+7. Attribute Information:
+   1. vendor name: 30 
+      (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, 
+       dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, 
+       microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, 
+       sratus, wang)
+   2. Model Name: many unique symbols
+   3. MYCT: machine cycle time in nanoseconds (integer)
+   4. MMIN: minimum main memory in kilobytes (integer)
+   5. MMAX: maximum main memory in kilobytes (integer)
+   6. CACH: cache memory in kilobytes (integer)
+   7. CHMIN: minimum channels in units (integer)
+   8. CHMAX: maximum channels in units (integer)
+   9. PRP: published relative performance (integer)
+  10. ERP: estimated relative performance from the original article (integer)
+
+8. Missing Attribute Values: None
+
+9. Class Distribution: the class value (PRP) is continuously valued.
+   PRP Value Range:   Number of Instances in Range:
+   0-20               31
+   21-100             121
+   101-200            27
+   201-300            13
+   301-400            7
+   401-500            4
+   501-600            2
+   above 600          4
+
+Summary Statistics:
+	   Min  Max   Mean    SD      PRP Correlation
+   MCYT:   17   1500  203.8   260.3   -0.3071
+   MMIN:   64   32000 2868.0  3878.7   0.7949
+   MMAX:   64   64000 11796.1 11726.6  0.8630
+   CACH:   0    256   25.2    40.6     0.6626
+   CHMIN:  0    52    4.7     6.8      0.6089
+   CHMAX:  0    176   18.2    26.0     0.6052
+   PRP:    6    1150  105.6   160.8    1.0000
+   ERP:   15    1238  99.3    154.8    0.9665
+

demo/regression/mapfeat.py

@@ -0,0 +1,32 @@
+#!/usr/bin/python
+import sys
+
+fo = open( 'machine.txt', 'w' ) 
+cnt = 6
+fmap = {}
+for l in open( 'machine.data' ):
+    arr = l.split(',')
+    fo.write(arr[8])
+    for i in xrange( 0,6 ):
+        fo.write( ' %d:%s' %(i,arr[i+2]) )
+    
+    if arr[0] not in fmap:
+        fmap[arr[0]] = cnt
+        cnt += 1
+    
+    fo.write( ' %d:1' % fmap[arr[0]] )	
+    fo.write('\n')
+
+fo.close()
+
+# create feature map for machine data
+fo = open('featmap.txt', 'w')
+# list from machine.names
+names = ['vendor','MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX', 'PRP', 'ERP' ]; 
+
+for i in xrange(0,6):
+    fo.write( '%d\t%s\tint\n' % (i, names[i+1]))
+
+for v, k in sorted( fmap.iteritems(), key = lambda x:x[1] ):
+    fo.write( '%d\tvendor=%s\ti\n' % (k, v))
+fo.close()

demo/regression/mknfold.py

@@ -0,0 +1,29 @@
+#!/usr/bin/python
+import sys
+import random
+
+if len(sys.argv) < 2:
+    print 'Usage:<filename> <k> [nfold = 5]'
+    exit(0)
+
+random.seed( 10 )
+
+k = int( sys.argv[2] )
+if len(sys.argv) > 3:
+    nfold = int( sys.argv[3] )
+else:
+    nfold = 5
+
+fi = open( sys.argv[1], 'r' )
+ftr = open( sys.argv[1]+'.train', 'w' )
+fte = open( sys.argv[1]+'.test', 'w' )
+for l in fi:
+    if random.randint( 1 , nfold ) == k:
+        fte.write( l )
+    else:
+        ftr.write( l )
+
+fi.close()
+ftr.close()
+fte.close()
+

demo/regression/runexp.sh

@@ -0,0 +1,16 @@
+#!/bin/bash
+# map the data to features. For convenience we only use 7 original attributes and encode them as features in a trivial way 
+python mapfeat.py
+# split train and test
+python mknfold.py machine.txt 1
+# training and output the models
+../../xgboost machine.conf
+# output predictions of test data
+../../xgboost machine.conf task=pred model_in=0002.model
+# print the boosters of 0002.model in dump.raw.txt
+../../xgboost machine.conf task=dump model_in=0002.model name_dump=dump.raw.txt
+# print the boosters of 0002.model in dump.nice.txt with feature map
+../../xgboost machine.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt 
+
+# cat the result
+cat dump.nice.txt

regression/xgboost_reg.h

@@ -0,0 +1,403 @@
+#ifndef XGBOOST_REG_H
+#define XGBOOST_REG_H
+/*!
+* \file xgboost_reg.h
+* \brief class for gradient boosted regression
+* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
+*/
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include "xgboost_reg_data.h"
+#include "xgboost_reg_eval.h"
+#include "../utils/xgboost_omp.h"
+#include "../booster/xgboost_gbmbase.h"
+#include "../utils/xgboost_utils.h"
+#include "../utils/xgboost_stream.h"
+
+namespace xgboost{
+    namespace regression{
+        /*! \brief class for gradient boosted regression */
+        class RegBoostLearner{
+        public:
+            /*! \brief constructor */
+            RegBoostLearner( void ){
+                silent = 0; 
+            }
+            /*! 
+            * \brief a regression booter associated with training and evaluating data 
+            * \param train pointer to the training data
+            * \param evals array of evaluating data
+            * \param evname name of evaluation data, used print statistics
+            */
+            RegBoostLearner( const DMatrix *train,
+                             const std::vector<DMatrix *> &evals,
+                             const std::vector<std::string> &evname ){
+                silent = 0;
+                this->SetData(train,evals,evname);
+            }
+
+            /*! 
+            * \brief associate regression booster with training and evaluating data 
+            * \param train pointer to the training data
+            * \param evals array of evaluating data
+            * \param evname name of evaluation data, used print statistics
+            */
+            inline void SetData( const DMatrix *train,
+                                 const std::vector<DMatrix *> &evals,
+                                 const std::vector<std::string> &evname ){
+                this->train_ = train;
+                this->evals_ = evals;
+                this->evname_ = evname; 
+                // estimate feature bound
+                int num_feature = (int)(train->data.NumCol());
+                // assign buffer index
+                unsigned buffer_size = static_cast<unsigned>( train->Size() );
+                
+                for( size_t i = 0; i < evals.size(); ++ i ){
+                    buffer_size += static_cast<unsigned>( evals[i]->Size() );
+                    num_feature = std::max( num_feature, (int)(evals[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 );
+                }
+                
+                // set eval_preds tmp sapce
+                this->eval_preds_.resize( evals.size(), std::vector<float>() );
+            }
+            /*! 
+            * \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 );                
+                mparam.SetParam( name, val );
+                base_gbm.SetParam( name, 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 ){
+                base_gbm.InitTrainer();
+                if( mparam.loss_type == kLogisticClassify ){
+                    evaluator_.AddEval( "error" );
+                }else{
+                    evaluator_.AddEval( "rmse" );
+                }
+                evaluator_.Init();
+            } 
+            /*!
+            * \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();
+            }
+            /*! 
+            * \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 );
+            }
+            /*! 
+             * \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) );
+            } 
+            /*! 
+             * \brief update the model for one iteration
+             * \param iteration iteration number
+             */
+            inline void UpdateOneIter( int iter ){
+                this->PredictBuffer( preds_, *train_, 0 );
+                this->GetGradient( preds_, train_->labels, grad_, hess_ );
+                std::vector<unsigned> root_index;
+                base_gbm.DoBoost( grad_, hess_, train_->data, root_index );                
+            }
+            /*! 
+             * \brief evaluate the model for specific iteration
+             * \param iter iteration number
+             * \param fo file to output log
+             */            
+            inline void EvalOneIter( int iter, FILE *fo = stderr ){
+                fprintf( fo, "[%d]", iter );
+                int buffer_offset = static_cast<int>( train_->Size() );
+                
+                for( size_t i = 0; i < evals_.size(); ++i ){
+                    std::vector<float> &preds = this->eval_preds_[ i ];
+                    this->PredictBuffer( preds, *evals_[i], buffer_offset);
+                    evaluator_.Eval( fo, evname_[i].c_str(), preds, (*evals_[i]).labels );
+                    buffer_offset += static_cast<int>( evals_[i]->Size() );
+                }
+                fprintf( fo,"\n" );
+            }
+            /*! \brief get prediction, without buffering */
+            inline void Predict( std::vector<float> &preds, const DMatrix &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.PredTransform
+                        ( mparam.base_score + base_gbm.Predict( data.data, j, -1 ) );
+                }
+            }
+        public:
+            /*! 
+             * \brief update the model for one iteration
+             * \param iteration iteration number
+             */
+            inline void UpdateInteract( std::string action ){
+                this->InteractPredict( preds_, *train_, 0 ); 
+
+                int buffer_offset = static_cast<int>( train_->Size() );
+                for( size_t i = 0; i < evals_.size(); ++i ){
+                    std::vector<float> &preds = this->eval_preds_[ i ];                
+                    this->InteractPredict( preds, *evals_[i], buffer_offset );
+                    buffer_offset += static_cast<int>( evals_[i]->Size() );
+                }
+
+                if( action == "remove" ){
+                    base_gbm.DelteBooster(); return;
+                }
+                
+                this->GetGradient( preds_, train_->labels, grad_, hess_ );
+                std::vector<unsigned> root_index;
+                base_gbm.DoBoost( grad_, hess_, train_->data, root_index );
+
+                this->InteractRePredict( *train_, 0 );
+                buffer_offset = static_cast<int>( train_->Size() );
+                for( size_t i = 0; i < evals_.size(); ++i ){
+                    this->InteractRePredict( *evals_[i], buffer_offset );
+                    buffer_offset += static_cast<int>( evals_[i]->Size() );
+                }
+            }
+        private:
+            /*! \brief get the transformed predictions, given data */
+            inline void InteractPredict( std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset ){
+                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.PredTransform
+                        ( mparam.base_score + base_gbm.InteractPredict( data.data, j, buffer_offset + j ) );
+                }
+            }
+            /*! \brief repredict trial */
+            inline void InteractRePredict( const DMatrix &data, unsigned buffer_offset ){
+                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 );
+                }
+            }
+        private:
+            /*! \brief get the transformed predictions, given data */
+            inline void PredictBuffer( std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset ){
+                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.PredTransform
+                        ( mparam.base_score + base_gbm.Predict( data.data, j, buffer_offset + j ) );
+                }
+            }
+
+            /*! \brief get the first order and second order gradient, given the transformed predictions and labels */
+            inline void GetGradient( const std::vector<float> &preds, 
+                                     const std::vector<float> &labels, 
+                                     std::vector<float> &grad,
+                                     std::vector<float> &hess ){
+                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 ){
+                    grad[j] = mparam.FirstOrderGradient( preds[j], labels[j] );
+                    hess[j] = mparam.SecondOrderGradient( preds[j], labels[j] );
+                }
+            }
+            
+        private:
+            enum LossType{
+                kLinearSquare = 0,
+                kLogisticNeglik = 1,
+                kLogisticClassify = 2
+            };
+
+            /*! \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 reserved field */
+                int reserved[ 16 ];
+                /*! \brief constructor */
+                ModelParam( void ){
+                    base_score = 0.5f;
+                    loss_type  = 0;
+                    num_feature = 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("loss_type", name ) )   loss_type = atoi( val );
+                    if( !strcmp("bst:num_feature", name ) ) num_feature = atoi( val );
+                }
+                /*! 
+                * \brief adjust base_score
+                */                
+                inline void AdjustBase( void ){
+                    if( loss_type == 1 || loss_type == 2 ){
+                        utils::Assert( base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain" );
+                        base_score = - logf( 1.0f / base_score - 1.0f );
+                    }
+                }
+
+                /*! 
+                * \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 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 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 kLogisticClassify:
+                    case kLogisticNeglik: return predt * ( 1 - predt );
+                    default: utils::Error("unknown loss_type"); return 0.0f;
+                    }
+                }
+
+                /*!
+                 * \brief calculating the loss, given the predictions, labels and the loss type
+                 * \param preds the given predictions
+                 * \param labels the given labels
+                 * \return the specified loss
+                 */
+                inline float Loss(const std::vector<float> &preds, const std::vector<float> &labels) const{
+                    switch( loss_type ){
+                    case kLinearSquare: return SquareLoss(preds,labels);
+                    case kLogisticNeglik: 
+                    case kLogisticClassify: return NegLoglikelihoodLoss(preds,labels);
+                    default: utils::Error("unknown loss_type"); return 0.0f;
+                    }
+                }
+
+                /*!
+                 * \brief calculating the square loss, given the predictions and labels
+                 * \param preds the given predictions
+                 * \param labels the given labels
+                 * \return the summation of square loss
+                 */
+                inline float SquareLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
+                    float ans = 0.0;
+                    for(size_t i = 0; i < preds.size(); i++){
+                        float dif = preds[i] - labels[i];
+                        ans += dif * dif;
+                    }
+                    return ans;
+                }
+
+                /*!
+                 * \brief calculating the square loss, given the predictions and labels
+                 * \param preds the given predictions
+                 * \param labels the given labels
+                 * \return the summation of square loss
+                 */
+                inline float NegLoglikelihoodLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
+                    float ans = 0.0;
+                    for(size_t i = 0; i < preds.size(); i++)
+                        ans -= labels[i] * logf(preds[i]) + ( 1 - labels[i] ) * logf(1 - preds[i]);
+                    return ans;
+                }
+            };
+        private:
+            int silent;
+            EvalSet evaluator_;
+            booster::GBMBase base_gbm;
+            ModelParam   mparam;
+            const DMatrix *train_;
+            std::vector<DMatrix *> evals_;
+            std::vector<std::string> evname_;
+            std::vector<unsigned> buffer_index_;
+        private:
+            std::vector<float> grad_, hess_, preds_;
+            std::vector< std::vector<float> > eval_preds_;
+        };
+    }
+};
+
+#endif

regression/xgboost_reg_data.h

@@ -0,0 +1,155 @@
+#ifndef XGBOOST_REG_DATA_H
+#define XGBOOST_REG_DATA_H
+
+/*!
+ * \file xgboost_reg_data.h
+ * \brief input data structure for regression and binary classification task.
+ *     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]+
+ * \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
+ */
+#include <cstdio>
+#include <vector>
+#include "../booster/xgboost_data.h"
+#include "../utils/xgboost_utils.h"
+#include "../utils/xgboost_stream.h"
+
+namespace xgboost{
+    namespace regression{
+        /*! \brief data matrix for regression content */
+        struct DMatrix{
+        public:
+            /*! \brief maximum feature dimension */
+            unsigned num_feature;
+            /*! \brief feature data content */
+            booster::FMatrixS data;
+            /*! \brief label of each instance */
+            std::vector<float> labels;
+        public:
+            /*! \brief default constructor */
+            DMatrix( void ){}
+
+            /*! \brief get the number of instances */
+            inline size_t Size() const{
+                return labels.size();
+            }
+            /*! 
+            * \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 ){
+                            labels.push_back( label );
+                            data.AddRow( findex, fvalue );
+                        }
+                        findex.clear(); fvalue.clear();
+                        utils::Assert( sscanf( tmp, "%f", &label ) == 1, "invalid format" );
+                        init = false;
+                    }
+                }
+
+                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);
+            }
+            /*! 
+            * \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 );
+                labels.resize( data.NumRow() );
+                utils::Assert( fs.Read( &labels[0], sizeof(float) * data.NumRow() ) != 0, "DMatrix LoadBinary" );
+                fs.Close();
+                // 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 );
+                }
+                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 );
+                fs.Write( &labels[0], sizeof(float) * data.NumRow() );
+                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 );
+                }
+            }
+            /*! 
+            * \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") ){
+                    this->LoadBinary( fname, silent ); 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:
+            /*! \brief update num_feature info */
+            inline void UpdateInfo( void ){
+                this->num_feature = 0;
+                for( size_t i = 0; i < data.NumRow(); i ++ ){
+                    booster::FMatrixS::Line sp = data[i];
+                    for( unsigned j = 0; j < sp.len; j ++ ){
+                        if( num_feature <= sp[j].findex ){
+                            num_feature = sp[j].findex + 1;
+                        }
+                    }
+                }
+            }
+        };
+    };
+};
+#endif

regression/xgboost_reg_eval.h

@@ -0,0 +1,119 @@
+#ifndef XGBOOST_REG_EVAL_H
+#define XGBOOST_REG_EVAL_H
+/*!
+* \file xgboost_reg_eval.h
+* \brief evaluation metrics for regression and classification
+* \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"
+
+namespace xgboost{
+    namespace regression{
+        /*! \brief evaluator that evaluates the loss metrics */
+        struct IEvaluator{
+            /*! 
+             * \brief evaluate a specific metric 
+             * \param preds prediction
+             * \param labels label
+             */
+            virtual float Eval( const std::vector<float> &preds, 
+                                const std::vector<float> &labels ) const= 0;
+            /*! \return name of metric */
+            virtual const char *Name( void ) const= 0;
+        };
+
+        /*! \brief RMSE */
+        struct EvalRMSE : public IEvaluator{            
+            virtual float Eval( const std::vector<float> &preds, 
+                                const std::vector<float> &labels ) const{
+                const unsigned ndata = static_cast<unsigned>( preds.size() );
+                float sum = 0.0;
+                #pragma omp parallel for reduction(+:sum) schedule( static )
+                for( unsigned i = 0; i < ndata; ++ i ){
+                    float diff = preds[i] - labels[i];
+                    sum += diff * diff;
+                } 
+                return sqrtf( sum / ndata );
+            }
+            virtual const char *Name( void ) const{
+                return "rmse";
+            }
+        };
+
+        /*! \brief Error */
+        struct EvalError : public IEvaluator{            
+            virtual float Eval( const std::vector<float> &preds, 
+                                const std::vector<float> &labels ) const{
+                const unsigned ndata = static_cast<unsigned>( preds.size() );
+                unsigned nerr = 0;
+                #pragma omp parallel for reduction(+:nerr) schedule( static )
+                for( unsigned i = 0; i < ndata; ++ i ){
+                    if( preds[i] > 0.5f ){
+                        if( labels[i] < 0.5f ) nerr += 1;
+                    }else{
+                        if( labels[i] > 0.5f ) nerr += 1;
+                    }
+                } 
+                return static_cast<float>(nerr) / ndata;
+            }
+            virtual const char *Name( void ) const{
+                return "error";
+            }
+        };
+
+
+        /*! \brief Error */
+        struct EvalLogLoss : public IEvaluator{            
+            virtual float Eval( const std::vector<float> &preds, 
+                                const std::vector<float> &labels ) const{
+                const unsigned ndata = static_cast<unsigned>( preds.size() );
+                unsigned nerr = 0;
+                #pragma omp parallel for reduction(+:nerr) schedule( static )
+                for( unsigned i = 0; i < ndata; ++ i ){
+                    const float y = labels[i];
+                    const float py = preds[i];
+                    nerr -= y * std::log(py) + (1.0f-y)*std::log(1-py);
+                } 
+                return static_cast<float>(nerr) / ndata;
+            }
+            virtual const char *Name( void ) const{
+                return "negllik";
+            }
+        };
+    };
+
+    namespace regression{
+        /*! \brief a set of evaluators */
+        struct EvalSet{
+        public:
+            inline void AddEval( const char *name ){                
+                if( !strcmp( name, "rmse") ) evals_.push_back( &rmse_ );
+                if( !strcmp( name, "error") ) evals_.push_back( &error_ );
+                if( !strcmp( name, "logloss") ) evals_.push_back( &logloss_ );
+            }
+            inline void Init( void ){
+                std::sort( evals_.begin(), evals_.end() );
+                evals_.resize( std::unique( evals_.begin(), evals_.end() ) - evals_.begin() );
+            }
+            inline void Eval( FILE *fo, const char *evname,
+                              const std::vector<float> &preds, 
+                              const std::vector<float> &labels ) const{
+                for( size_t i = 0; i < evals_.size(); ++ i ){
+                    float res = evals_[i]->Eval( preds, labels );
+                    fprintf( fo, "\t%s-%s:%f", evname, evals_[i]->Name(), res ); 
+                } 
+            }
+        private:
+            EvalRMSE  rmse_;
+            EvalError error_;
+            EvalLogLoss logloss_;
+            std::vector<const IEvaluator*> evals_;  
+        };
+    };
+};
+#endif

regression/xgboost_reg_main.cpp

@@ -0,0 +1,280 @@
+#define _CRT_SECURE_NO_WARNINGS
+#define _CRT_SECURE_NO_DEPRECATE
+
+#include <ctime>
+#include <string>
+#include <cstring>
+#include "xgboost_reg.h"
+#include "../utils/xgboost_fmap.h"
+#include "../utils/xgboost_random.h"
+#include "../utils/xgboost_config.h"
+
+namespace xgboost{
+    namespace regression{
+        /*!
+        * \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("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;
+                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 );
+                    }
+                }
+                learner.SetData( &data, deval, eval_data_names );
+            }
+            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( i );
+                    learner.EvalOneIter( i );
+                    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 ){
+                    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 );
+            }
+            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 );
+                        batch_action += 1;
+                    } else{
+                        learner.SetParam( cfg_batch.name(), cfg_batch.val() );
+                    }
+                }
+
+                if( batch_action == 0 ){
+                    learner.UpdateInteract( interact_action );
+                }
+                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 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;
+            utils::FeatMap fmap;
+            RegBoostLearner learner;
+        };
+    };
+};
+
+int main( int argc, char *argv[] ){
+    xgboost::random::Seed( 0 );
+    xgboost::regression::RegBoostTask tsk;
+    return tsk.Run( argc, argv );
+}

utils/xgboost_config.h

@@ -0,0 +1,214 @@
+#ifndef XGBOOST_CONFIG_H
+#define XGBOOST_CONFIG_H
+/*!
+ * \file xgboost_config.h
+ * \brief helper class to load in configures from file
+ * \author Tianqi Chen: tianqi.tchen@gmail.com
+ */
+#define _CRT_SECURE_NO_WARNINGS
+#include <cstdio>
+#include <cstring>
+#include <string>
+#include "xgboost_utils.h"
+#include <vector>
+
+namespace xgboost{
+    namespace utils{
+        /*! 
+         * \brief an iterator that iterates over a configure file and gets the configures
+         */
+        class ConfigIterator{
+        public:
+            /*! 
+             * \brief constructor 
+             * \param fname name of configure file
+             */
+            ConfigIterator( const char *fname ){
+                fi = FopenCheck( fname, "r");
+                ch_buf = fgetc( fi );
+            }
+            /*! \brief destructor */
+            ~ConfigIterator(){
+                fclose( fi );
+            }
+            /*! 
+             * \brief get current name, called after Next returns true
+             * \return current parameter name 
+             */
+            inline const char *name( void )const{
+                return s_name;
+            }
+            /*! 
+             * \brief get current value, called after Next returns true
+             * \return current parameter value 
+             */
+            inline const char *val( void ) const{
+                return s_val;
+            }
+            /*! 
+             * \brief move iterator to next position
+             * \return true if there is value in next position
+             */
+            inline bool Next( void ){
+                while( !feof( fi ) ){
+                    GetNextToken( s_name );
+                    if( s_name[0] == '=')  return false;               
+                    if( GetNextToken( s_buf ) || s_buf[0] != '=' ) return false;
+                    if( GetNextToken( s_val ) || s_val[0] == '=' ) return false;
+                    return true;
+                }
+                return false;
+            }
+        private:
+            FILE *fi;        
+            char ch_buf;
+            char s_name[256],s_val[256],s_buf[246];
+            
+            inline void SkipLine(){           
+                do{
+                    ch_buf = fgetc( fi );
+                }while( ch_buf != EOF && ch_buf != '\n' && ch_buf != '\r' );
+            }
+            
+            inline void ParseStr( char tok[] ){
+                int i = 0; 
+                while( (ch_buf = fgetc(fi)) != EOF ){
+                    switch( ch_buf ){
+                    case '\\': tok[i++] = fgetc( fi ); break;
+                    case '\"': tok[i++] = '\0'; 
+						return;                        
+                    case '\r':
+                    case '\n': Error("unterminated string"); break;
+                    default: tok[i++] = ch_buf;
+                    }
+                }
+                Error("unterminated string"); 
+            }
+            // return newline 
+            inline bool GetNextToken( char tok[] ){
+                int i = 0;
+                bool new_line = false; 
+                while( ch_buf != EOF ){
+                    switch( ch_buf ){
+                    case '#' : SkipLine(); new_line = true; break;
+                    case '\"':
+                        if( i == 0 ){
+                            ParseStr( tok );ch_buf = fgetc(fi); return new_line;
+                        }else{
+                            Error("token followed directly by string"); 
+                        }
+                    case '=':
+                        if( i == 0 ) {
+                            ch_buf = fgetc( fi );     
+                            tok[0] = '='; 
+                            tok[1] = '\0'; 
+                        }else{
+                            tok[i] = '\0'; 
+                        }
+                        return new_line;
+                case '\r':
+                    case '\n':
+					if( i == 0 ) new_line = true;
+                    case '\t':
+                    case ' ' :
+                        ch_buf = fgetc( fi );
+                        if( i > 0 ){
+                            tok[i] = '\0'; 
+                            return new_line;
+                        }               
+                        break;
+                    default: 
+                        tok[i++] = ch_buf;
+                        ch_buf = fgetc( fi );
+                        break;                    
+                    }
+                }
+                return true;
+            }
+        };
+    };
+    
+    namespace utils{
+        /*! 
+         * \brief a class that save parameter configurations 
+         *        temporally and allows to get them out later 
+         *        there are two kinds of priority in ConfigSaver
+         */
+        class ConfigSaver{
+        public:
+            /*! \brief constructor */
+            ConfigSaver( void ){ idx = 0; }
+            /*! \brief clear all saves */
+            inline void Clear( void ){
+                idx = 0;
+                names.clear(); values.clear();
+                names_high.clear(); values_high.clear();
+            }
+            /*! 
+             * \brief push back a parameter setting 
+             * \param name name of parameter
+             * \param val  value of parameter
+             * \param priority whether the setting has higher priority: high priority occurs 
+             *        latter when read from ConfigSaver, and can overwrite existing settings
+             */
+            inline void PushBack( const char *name, const char *val, int priority = 0 ){
+                if( priority == 0 ){
+                    names.push_back( std::string( name ) );
+                    values.push_back( std::string( val ) );
+                }else{
+                    names_high.push_back( std::string( name ) );
+                    values_high.push_back( std::string( val ) );
+                }
+            }
+            /*! \brief set pointer to beginning of the ConfigSaver */
+            inline void BeforeFirst( void ){
+                idx = 0;
+            }
+            /*! 
+             * \brief move iterator to next position
+             * \return true if there is value in next position
+             */
+            inline bool Next( void ){
+                if( idx >= names.size() + names_high.size() ){
+                    return false;
+                }
+                idx ++;
+                return true;
+            }
+            /*! 
+             * \brief get current name, called after Next returns true
+             * \return current parameter name 
+             */            
+            inline const char *name( void ) const{
+                Assert( idx > 0, "can't call name before first");
+                size_t i = idx - 1;
+                if( i >= names.size() ){
+                    return names_high[ i - names.size() ].c_str();
+                }else{
+                    return names[ i ].c_str();
+                }
+            }
+            /*! 
+             * \brief get current value, called after Next returns true
+             * \return current parameter value 
+             */
+            inline const char *val( void ) const{
+                Assert( idx > 0, "can't call name before first");
+                size_t i = idx - 1;
+                if( i >= values.size() ){
+                    return values_high[ i - values.size() ].c_str();
+                }else{
+                    return values[ i ].c_str();
+                }
+            }
+        private:
+            std::vector<std::string> names;
+            std::vector<std::string> values;
+            std::vector<std::string> names_high;
+            std::vector<std::string> values_high;        
+            size_t idx;
+        };
+    };
+};
+#endif
+

utils/xgboost_fmap.h

@@ -0,0 +1,123 @@
+#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[256], ftype[256];
+                while( fscanf( fi, "%d%s%s", &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

utils/xgboost_matrix_csr.h

@@ -0,0 +1,155 @@
+/*!
+ * \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

utils/xgboost_omp.h

@@ -0,0 +1,18 @@
+#ifndef XGBOOST_OMP_H
+#define XGBOOST_OMP_H
+/*!
+ * \file xgboost_omp.h
+ * \brief header to handle OpenMP compatibility issues
+ * 
+ * \author Tianqi Chen: tianqi.tchen@gmail.com
+ */
+
+#if defined(_OPENMP)
+#include <omp.h>
+#else
+#warning "OpenMP is not available, compile to single thread code"
+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

utils/xgboost_random.h

@@ -0,0 +1,131 @@
+#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() );
+        }
+    };
+};
+
+#endif

utils/xgboost_stream.h

@@ -0,0 +1,54 @@
+#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

utils/xgboost_utils.h

@@ -0,0 +1,68 @@
+#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 );
+            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 );
+                exit( -1 );
+            }
+            return fp;
+        }       
+    };
+};
+
+#endif