modify tree so that training is standalone
This commit is contained in:
tqchen
parent
2c6922f432
commit
4a612eb3ba
2
Makefile
2
Makefile
@ -1,6 +1,6 @@
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export CC = gcc
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export CXX = g++
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export CFLAGS = -Wall -O3 -msse2
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export CFLAGS = -Wall -O3 -msse2 -fopenmp
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# specify tensor path
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BIN = xgboost
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@ -3,7 +3,7 @@
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/*!
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* \file xgboost_linear.h
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* \brief Implementation of Linear booster, with L1/L2 regularization: Elastic Net
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* the update rule is coordinate descent
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* the update rule is coordinate descent, require column major format
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* \author Tianqi Chen: tianqi.tchen@gmail.com
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*/
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#include <vector>
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@ -11,7 +11,6 @@
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#include "../xgboost.h"
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#include "../../utils/xgboost_utils.h"
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#include "../../utils/xgboost_matrix_csr.h"
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namespace xgboost{
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namespace booster{
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@ -41,7 +40,7 @@ namespace xgboost{
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const FMatrixS &smat,
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const std::vector<unsigned> &root_index ){
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utils::Assert( grad.size() < UINT_MAX, "number of instance exceed what we can handle" );
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this->Update( smat, grad, hess );
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this->UpdateWeights( grad, hess, smat );
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}
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virtual float Predict( const FMatrixS::Line &sp, unsigned rid = 0 ){
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float sum = model.bias();
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@ -149,29 +148,17 @@ namespace xgboost{
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return weight.back();
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}
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};
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/*! \brief array entry for column based feature construction */
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struct SCEntry{
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/*! \brief feature value */
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float fvalue;
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/*! \brief row index related to each row */
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unsigned rindex;
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/*! \brief default constructor */
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SCEntry( void ){}
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/*! \brief constructor using entry */
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SCEntry( float fvalue, unsigned rindex ){
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this->fvalue = fvalue; this->rindex = rindex;
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}
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};
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private:
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int silent;
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protected:
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Model model;
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ParamTrain param;
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protected:
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// update weights, should work for any FMatrix
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template<typename FMatrix>
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inline void UpdateWeights( std::vector<float> &grad,
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const std::vector<float> &hess,
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const std::vector<size_t> &rptr,
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const std::vector<SCEntry> &entry ){
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const FMatrix &smat ){
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{// optimize bias
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double sum_grad = 0.0, sum_hess = 0.0;
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for( size_t i = 0; i < grad.size(); i ++ ){
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@ -187,70 +174,25 @@ namespace xgboost{
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}
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// optimize weight
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const int nfeat = model.param.num_feature;
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for( int i = 0; i < nfeat; i ++ ){
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size_t start = rptr[i];
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size_t end = rptr[i+1];
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if( start >= end ) continue;
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const unsigned nfeat= (unsigned)smat.NumCol();
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for( unsigned i = 0; i < nfeat; i ++ ){
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if( !smat.GetSortedCol( i ).Next() ) continue;
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double sum_grad = 0.0, sum_hess = 0.0;
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for( size_t j = start; j < end; j ++ ){
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const float v = entry[j].fvalue;
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sum_grad += grad[ entry[j].rindex ] * v;
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sum_hess += hess[ entry[j].rindex ] * v * v;
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for( typename FMatrix::ColIter it = smat.GetSortedCol(i); it.Next(); ){
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const float v = it.fvalue();
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sum_grad += grad[ it.rindex() ] * v;
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sum_hess += hess[ it.rindex() ] * v * v;
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}
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float w = model.weight[ i ];
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double dw = param.learning_rate * param.CalcDelta( sum_grad, sum_hess, w );
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model.weight[ i ] += dw;
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// update grad value
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for( size_t j = start; j < end; j ++ ){
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const float v = entry[j].fvalue;
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grad[ entry[j].rindex ] += hess[ entry[j].rindex ] * v * dw;
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for( typename FMatrix::ColIter it = smat.GetSortedCol(i); it.Next(); ){
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const float v = it.fvalue();
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grad[ it.rindex() ] += hess[ it.rindex() ] * v * dw;
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}
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}
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}
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inline void MakeCmajor( std::vector<size_t> &rptr,
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std::vector<SCEntry> &entry,
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const std::vector<float> &hess,
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const FMatrixS &smat ){
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// transform to column order first
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const int nfeat = model.param.num_feature;
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// build CSR column major format data
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utils::SparseCSRMBuilder<SCEntry> builder( rptr, entry );
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builder.InitBudget( nfeat );
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for( unsigned i = 0; i < (unsigned)hess.size(); i ++ ){
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// skip deleted entries
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if( hess[i] < 0.0f ) continue;
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// add sparse part budget
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FMatrixS::Line sp = smat[ i ];
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for( unsigned j = 0; j < sp.len; j ++ ){
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if( j == 0 || sp[j-1].findex != sp[j].findex ){
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builder.AddBudget( sp[j].findex );
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}
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}
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}
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builder.InitStorage();
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for( unsigned i = 0; i < (unsigned)hess.size(); i ++ ){
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// skip deleted entries
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if( hess[i] < 0.0f ) continue;
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// add sparse part budget
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FMatrixS::Line sp = smat[ i ];
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for( unsigned j = 0; j < sp.len; j ++ ){
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// skip duplicated terms
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if( j == 0 || sp[j-1].findex != sp[j].findex ){
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builder.PushElem( sp[j].findex, SCEntry( sp[j].fvalue, i ) );
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}
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}
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}
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}
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protected:
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virtual void Update( const FMatrixS &smat,
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std::vector<float> &grad,
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const std::vector<float> &hess ){
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std::vector<size_t> rptr;
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std::vector<SCEntry> entry;
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this->MakeCmajor( rptr, entry, hess, smat );
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this->UpdateWeights( grad, hess, rptr, entry );
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}
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};
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};
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};
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@ -2,7 +2,7 @@
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#define _XGBOOST_APEX_TREE_HPP_
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/*!
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* \file xgboost_svdf_tree.hpp
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* \brief implementation of regression tree, with layerwise support
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* \brief implementation of regression tree constructor, with layerwise support
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* this file is adapted from GBRT implementation in SVDFeature project
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* \author Tianqi Chen: tqchen@apex.sjtu.edu.cn, tianqi.tchen@gmail.com
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*/
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@ -12,18 +12,7 @@
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#include "../../utils/xgboost_matrix_csr.h"
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namespace xgboost{
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namespace booster{
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const bool rt_debug = false;
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// whether to check bugs
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const bool check_bug = false;
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const float rt_eps = 1e-5f;
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const float rt_2eps = rt_eps * 2.0f;
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inline double sqr( double a ){
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return a * a;
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}
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namespace booster{
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inline void assert_sorted( unsigned *idset, int len ){
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if( !rt_debug || !check_bug ) return;
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for( int i = 1; i < len; i ++ ){
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@ -32,21 +21,7 @@ namespace xgboost{
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}
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};
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namespace booster{
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// node stat used in rtree
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struct RTreeNodeStat{
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// loss chg caused by current split
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float loss_chg;
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// weight of current node
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float base_weight;
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// number of child that is leaf node known up to now
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int leaf_child_cnt;
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};
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// structure of Regression Tree
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class RTree: public TreeModel<float,RTreeNodeStat>{
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};
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namespace booster{
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// selecter of rtree to find the suitable candidate
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class RTSelecter{
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public:
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@ -88,7 +63,9 @@ namespace xgboost{
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}
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};
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// updater of rtree, allows the parameters to be stored inside, key solver
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template<typename FMatrix>
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class RTreeUpdater{
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protected:
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// training task, element of single task
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@ -128,10 +105,10 @@ namespace xgboost{
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// training parameter
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const TreeParamTrain ¶m;
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// parameters, reference
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RTree &tree;
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RegTree &tree;
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std::vector<float> &grad;
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std::vector<float> &hess;
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const FMatrixS &smat;
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const FMatrix &smat;
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const std::vector<unsigned> &group_id;
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private:
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// maximum depth up to now
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@ -158,7 +135,7 @@ namespace xgboost{
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inline void try_prune_leaf( int nid, int depth ){
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if( tree[ nid ].is_root() ) return;
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int pid = tree[ nid ].parent();
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RTree::NodeStat &s = tree.stat( pid );
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RegTree::NodeStat &s = tree.stat( pid );
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s.leaf_child_cnt ++;
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if( s.leaf_child_cnt >= 2 && param.need_prune( s.loss_chg, depth - 1 ) ){
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@ -186,7 +163,7 @@ namespace xgboost{
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// make split for current task, re-arrange positions in idset
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inline void make_split( Task tsk, const SCEntry *entry, int num, float loss_chg, double base_weight ){
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// before split, first prepare statistics
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RTree::NodeStat &s = tree.stat( tsk.nid );
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RegTree::NodeStat &s = tree.stat( tsk.nid );
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s.loss_chg = loss_chg;
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s.leaf_child_cnt = 0;
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s.base_weight = static_cast<float>( base_weight );
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@ -214,7 +191,7 @@ namespace xgboost{
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}
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}
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// get two parts
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RTree::Node &n = tree[ tsk.nid ];
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RegTree::Node &n = tree[ tsk.nid ];
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Task def_part( n.default_left() ? n.cleft() : n.cright(), tsk.idset, tsk.len - qset.size(), s.base_weight );
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Task spl_part( n.default_left() ? n.cright(): n.cleft() , tsk.idset + def_part.len, qset.size(), s.base_weight );
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// fill back split part
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@ -320,9 +297,8 @@ namespace xgboost{
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rsum_grad += grad[ ridx ];
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rsum_hess += hess[ ridx ];
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FMatrixS::Line sp = smat[ ridx ];
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for( unsigned j = 0; j < sp.len; j ++ ){
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builder.AddBudget( sp[j].findex );
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for( typename FMatrix::RowIter it = smat.GetRow(ridx); it.Next(); ){
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builder.AddBudget( it.findex() );
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}
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}
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@ -334,10 +310,9 @@ namespace xgboost{
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builder.InitStorage();
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for( unsigned i = 0; i < tsk.len; i ++ ){
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const unsigned ridx = tsk.idset[i];
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FMatrixS::Line sp = smat[ ridx ];
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for( unsigned j = 0; j < sp.len; j ++ ){
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builder.PushElem( sp[j].findex, SCEntry( sp[j].fvalue, ridx ) );
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}
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for( typename FMatrix::RowIter it = smat.GetRow(ridx); it.Next(); ){
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builder.PushElem( it.findex(), SCEntry( it.fvalue(), ridx ) );
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}
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}
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// --- end of building column major matrix ---
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// after this point, tmp_rptr and entry is ready to use
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@ -426,10 +401,10 @@ namespace xgboost{
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}
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public:
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RTreeUpdater( const TreeParamTrain &pparam,
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RTree &ptree,
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RegTree &ptree,
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std::vector<float> &pgrad,
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std::vector<float> &phess,
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const FMatrixS &psmat,
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const FMatrix &psmat,
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const std::vector<unsigned> &pgroup_id ):
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param( pparam ), tree( ptree ), grad( pgrad ), hess( phess ),
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smat( psmat ), group_id( pgroup_id ){
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@ -446,113 +421,6 @@ namespace xgboost{
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return max_depth;
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}
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};
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class RTreeTrainer : public IBooster{
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private:
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int silent;
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// tree of current shape
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RTree tree;
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TreeParamTrain param;
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private:
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std::vector<float> tmp_feat;
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std::vector<bool> tmp_funknown;
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inline void init_tmpfeat( void ){
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if( tmp_feat.size() != (size_t)tree.param.num_feature ){
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tmp_feat.resize( tree.param.num_feature );
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tmp_funknown.resize( tree.param.num_feature );
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std::fill( tmp_funknown.begin(), tmp_funknown.end(), true );
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}
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}
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public:
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virtual void SetParam( const char *name, const char *val ){
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if( !strcmp( name, "silent") ) silent = atoi( val );
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param.SetParam( name, val );
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tree.param.SetParam( name, val );
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}
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virtual void LoadModel( utils::IStream &fi ){
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tree.LoadModel( fi );
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}
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virtual void SaveModel( utils::IStream &fo ) const{
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tree.SaveModel( fo );
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}
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virtual void InitModel( void ){
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tree.InitModel();
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}
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private:
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inline int get_next( int pid, float fvalue, bool is_unknown ){
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float split_value = tree[ pid ].split_cond();
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if( is_unknown ){
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if( tree[ pid ].default_left() ) return tree[ pid ].cleft();
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else return tree[ pid ].cright();
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}else{
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if( fvalue < split_value ) return tree[ pid ].cleft();
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else return tree[ pid ].cright();
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}
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}
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public:
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virtual void DoBoost( std::vector<float> &grad,
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std::vector<float> &hess,
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const FMatrixS &smat,
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const std::vector<unsigned> &group_id ){
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utils::Assert( grad.size() < UINT_MAX, "number of instance exceed what we can handle" );
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if( !silent ){
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printf( "\nbuild GBRT with %u instances\n", (unsigned)grad.size() );
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}
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// start with a id set
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RTreeUpdater updater( param, tree, grad, hess, smat, group_id );
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int num_pruned;
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tree.param.max_depth = updater.do_boost( num_pruned );
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if( !silent ){
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printf( "tree train end, %d roots, %d extra nodes, %d pruned nodes ,max_depth=%d\n",
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tree.param.num_roots, tree.num_extra_nodes(), num_pruned, tree.param.max_depth );
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}
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}
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virtual int GetLeafIndex( const std::vector<float> &feat,
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const std::vector<bool> &funknown,
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unsigned gid = 0 ){
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// start from groups that belongs to current data
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int pid = (int)gid;
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// tranverse tree
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while( !tree[ pid ].is_leaf() ){
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unsigned split_index = tree[ pid ].split_index();
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pid = this->get_next( pid, feat[ split_index ], funknown[ split_index ] );
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}
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return pid;
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}
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virtual float Predict( const FMatrixS::Line &feat, unsigned gid = 0 ){
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this->init_tmpfeat();
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for( unsigned i = 0; i < feat.len; i ++ ){
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utils::Assert( feat[i].findex < (unsigned)tmp_funknown.size() , "input feature execeed bound" );
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tmp_funknown[ feat[i].findex ] = false;
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tmp_feat[ feat[i].findex ] = feat[i].fvalue;
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}
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int pid = this->GetLeafIndex( tmp_feat, tmp_funknown, gid );
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// set back
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for( unsigned i = 0; i < feat.len; i ++ ){
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tmp_funknown[ feat[i].findex ] = true;
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}
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return tree[ pid ].leaf_value();
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}
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virtual float Predict( const std::vector<float> &feat,
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const std::vector<bool> &funknown,
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unsigned gid = 0 ){
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utils::Assert( feat.size() >= (size_t)tree.param.num_feature,
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"input data smaller than num feature" );
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int pid = this->GetLeafIndex( feat, funknown, gid );
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return tree[ pid ].leaf_value();
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}
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virtual void DumpModel( FILE *fo ){
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tree.DumpModel( fo );
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}
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public:
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RTreeTrainer( void ){ silent = 0; }
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virtual ~RTreeTrainer( void ){}
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};
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};
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};
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#endif
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@ -306,7 +306,7 @@ namespace xgboost{
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}
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};
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};
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namespace booster{
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/*! \brief training parameters for regression tree */
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struct TreeParamTrain{
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@ -431,5 +431,20 @@ namespace xgboost{
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}
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};
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};
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namespace booster{
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/*! \brief node statistics used in regression tree */
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struct RTreeNodeStat{
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// loss chg caused by current split
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float loss_chg;
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// weight of current node
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float base_weight;
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// number of child that is leaf node known up to now
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int leaf_child_cnt;
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};
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/*! \brief most comment structure of regression tree */
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class RegTree: public TreeModel<bst_float,RTreeNodeStat>{
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};
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};
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};
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#endif
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@ -11,18 +11,11 @@
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#include "../utils/xgboost_utils.h"
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#include "xgboost_gbmbase.h"
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// implementations of boosters
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#include "tree/xgboost_svdf_tree.hpp"
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#include "tree/xgboost_tree.hpp"
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#include "linear/xgboost_linear.hpp"
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namespace xgboost{
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namespace booster{
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/*
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* \brief listing the types of boosters
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*/
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enum BOOSTER_TYPE_LIST{
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TREE,
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LINEAR,
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};
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/*!
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* \brief create a gradient booster, given type of booster
|
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* \param booster_type type of gradient booster, can be used to specify implements
|
||||
@ -30,8 +23,8 @@ namespace xgboost{
|
||||
*/
|
||||
IBooster *CreateBooster( int booster_type ){
|
||||
switch( booster_type ){
|
||||
case TREE: return new RTreeTrainer();
|
||||
case LINEAR: return new LinearBooster();
|
||||
case 0: return new RegTreeTrainer();
|
||||
case 1: return new LinearBooster();
|
||||
default: utils::Error("unknown booster_type"); return NULL;
|
||||
}
|
||||
}
|
||||
|
||||
@ -11,6 +11,7 @@
|
||||
#include <climits>
|
||||
#include "../utils/xgboost_utils.h"
|
||||
#include "../utils/xgboost_stream.h"
|
||||
#include "../utils/xgboost_matrix_csr.h"
|
||||
|
||||
namespace xgboost{
|
||||
namespace booster{
|
||||
@ -66,10 +67,6 @@ namespace xgboost{
|
||||
inline bst_float fvalue( void ) const;
|
||||
};
|
||||
public:
|
||||
/*!
|
||||
* \brief prepare sorted columns so that GetSortedCol can be called
|
||||
*/
|
||||
inline void MakeSortedCol( void );
|
||||
/*!
|
||||
* \brief get number of rows
|
||||
* \return number of rows
|
||||
@ -114,13 +111,13 @@ namespace xgboost{
|
||||
bst_uint findex;
|
||||
/*! \brief feature value */
|
||||
bst_float fvalue;
|
||||
};
|
||||
/*! \brief one entry in a row */
|
||||
struct CEntry{
|
||||
/*! \brief row index */
|
||||
bst_uint rindex;
|
||||
/*! \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{
|
||||
@ -128,25 +125,32 @@ namespace xgboost{
|
||||
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];
|
||||
}
|
||||
};
|
||||
public:
|
||||
/*! \brief row iterator */
|
||||
struct RowIter{
|
||||
const REntry *dptr, *end;
|
||||
const REntry *dptr_, *end_;
|
||||
inline bool Next( void ){
|
||||
if( dptr == end ) return false;
|
||||
if( dptr_ == end_ ) return false;
|
||||
else{
|
||||
++ dptr; return true;
|
||||
++ dptr_; return true;
|
||||
}
|
||||
}
|
||||
inline bst_uint findex( void ) const{
|
||||
return dptr->findex;
|
||||
return dptr_->findex;
|
||||
}
|
||||
inline bst_float fvalue( void ) const{
|
||||
return dptr->fvalue;
|
||||
}
|
||||
return dptr_->fvalue;
|
||||
}
|
||||
};
|
||||
/*! \brief column iterator */
|
||||
struct ColIter: public RowIter{
|
||||
inline bst_uint rindex( void ) const{
|
||||
return this->findex();
|
||||
}
|
||||
};
|
||||
public:
|
||||
/*! \brief constructor */
|
||||
@ -167,6 +171,8 @@ namespace xgboost{
|
||||
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{
|
||||
@ -175,14 +181,6 @@ namespace xgboost{
|
||||
sp.len = static_cast<bst_uint>( row_ptr_[ sidx + 1 ] - row_ptr_[ sidx ] );
|
||||
sp.data_ = &row_data_[ row_ptr_[ sidx ] ];
|
||||
return sp;
|
||||
}
|
||||
/*! \brief get row iterator*/
|
||||
inline RowIter GetRow( size_t ridx ) const{
|
||||
utils::Assert( !bst_debug || ridx < this->NumRow(), "row id exceed bound" );
|
||||
RowIter it;
|
||||
it.dptr = &row_data_[ row_ptr_[ridx] ] - 1;
|
||||
it.dptr = &row_data_[ row_ptr_[ridx+1] ] - 1;
|
||||
return it;
|
||||
}
|
||||
/*!
|
||||
* \brief add a row to the matrix, with data stored in STL container
|
||||
@ -199,43 +197,124 @@ namespace xgboost{
|
||||
unsigned cnt = 0;
|
||||
for( size_t i = 0; i < findex.size(); i ++ ){
|
||||
if( findex[i] < fstart || findex[i] >= fend ) continue;
|
||||
REntry e; e.findex = findex[i]; e.fvalue = fvalue[i];
|
||||
row_data_.push_back( e );
|
||||
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" );
|
||||
RowIter it;
|
||||
it.dptr_ = &row_data_[ row_ptr_[ridx] ] - 1;
|
||||
it.end_ = &row_data_[ row_ptr_[ridx+1] ] - 1;
|
||||
return it;
|
||||
}
|
||||
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" );
|
||||
ColIter it;
|
||||
it.dptr_ = &col_data_[ col_ptr_[cidx] ] - 1;
|
||||
it.end_ = &col_data_[ col_ptr_[cidx+1] ] - 1;
|
||||
return it;
|
||||
}
|
||||
/*!
|
||||
* \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 row_ptr,
|
||||
* 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{
|
||||
size_t nrow = this->NumRow();
|
||||
fo.Write( &nrow, sizeof(size_t) );
|
||||
fo.Write( &row_ptr_[0], row_ptr_.size() * sizeof(size_t) );
|
||||
if( row_data_.size() != 0 ){
|
||||
fo.Write( &row_data_[0] , row_data_.size() * sizeof(REntry) );
|
||||
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 row_ptr,
|
||||
* the function is not consistent between 64bit and 32bit machine
|
||||
* \param fi output 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_ );
|
||||
}
|
||||
}
|
||||
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" );
|
||||
row_ptr_.resize( nrow + 1 );
|
||||
utils::Assert( fi.Read( &row_ptr_[0], row_ptr_.size() * sizeof(size_t) ), "Load FMatrixS" );
|
||||
ptr.resize( nrow + 1 );
|
||||
utils::Assert( fi.Read( &ptr[0], ptr.size() * sizeof(size_t) ), "Load FMatrixS" );
|
||||
|
||||
row_data_.resize( row_ptr_.back() );
|
||||
if( row_data_.size() != 0 ){
|
||||
utils::Assert( fi.Read( &row_data_[0] , row_data_.size() * sizeof(REntry) ) , "Load FMatrixS" );
|
||||
data.resize( ptr.back() );
|
||||
if( data.size() != 0 ){
|
||||
utils::Assert( fi.Read( &data[0] , data.size() * sizeof(REntry) ) , "Load FMatrixS" );
|
||||
}
|
||||
}
|
||||
private:
|
||||
@ -243,6 +322,10 @@ namespace xgboost{
|
||||
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_;
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
@ -65,11 +65,12 @@ namespace xgboost{
|
||||
|
||||
labels.push_back( label );
|
||||
data.AddRow( findex, fvalue );
|
||||
// initialize column support as well
|
||||
data.InitData();
|
||||
|
||||
this->UpdateInfo();
|
||||
if( !silent ){
|
||||
printf("%ux%u matrix with %lu entries is loaded from %s\n",
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname );
|
||||
}
|
||||
fclose(file);
|
||||
}
|
||||
@ -87,10 +88,12 @@ namespace xgboost{
|
||||
labels.resize( data.NumRow() );
|
||||
utils::Assert( fs.Read( &labels[0], sizeof(float) * data.NumRow() ) != 0, "DMatrix LoadBinary" );
|
||||
fs.Close();
|
||||
this->UpdateInfo();
|
||||
// initialize column support as well
|
||||
data.InitData();
|
||||
|
||||
if( !silent ){
|
||||
printf("%ux%u matrix with %lu entries is loaded from %s\n",
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname );
|
||||
}
|
||||
return true;
|
||||
}
|
||||
@ -100,13 +103,16 @@ namespace xgboost{
|
||||
* \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)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname );
|
||||
}
|
||||
}
|
||||
/*!
|
||||
|
||||
@ -43,13 +43,13 @@ namespace xgboost{
|
||||
}
|
||||
public:
|
||||
/*!
|
||||
* \brief step 1: initialize the number of rows in the data
|
||||
* \nrows number of rows in the matrix
|
||||
* \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 ){
|
||||
inline void InitBudget( size_t nrows = 0 ){
|
||||
if( !UseAcList ){
|
||||
rptr.resize( nrows + 1 );
|
||||
std::fill( rptr.begin(), rptr.end(), 0 );
|
||||
rptr.clear();
|
||||
rptr.resize( nrows + 1, 0 );
|
||||
}else{
|
||||
Assert( nrows + 1 == rptr.size(), "rptr must be initialized already" );
|
||||
this->Cleanup();
|
||||
@ -61,6 +61,9 @@ namespace xgboost{
|
||||
* \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 );
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user