add row tree maker, to be finished
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tqchen
149
booster/tree/xgboost_row_treemaker.hpp
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149
booster/tree/xgboost_row_treemaker.hpp
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#ifndef XGBOOST_ROW_TREEMAKER_HPP
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#define XGBOOST_ROW_TREEMAKER_HPP
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/*!
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* \file xgboost_row_treemaker.hpp
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* \brief implementation of regression tree maker,
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* use a row based approach
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* \author Tianqi Chen: tianqi.tchen@gmail.com
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*/
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// use openmp
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#include <vector>
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#include "xgboost_tree_model.h"
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#include "../../utils/xgboost_omp.h"
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#include "../../utils/xgboost_random.h"
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#include "xgboost_base_treemaker.hpp"
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namespace xgboost{
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namespace booster{
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template<typename FMatrix>
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class RowTreeMaker : protected BaseTreeMaker{
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public:
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RowTreeMaker( RegTree &tree,
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const TreeParamTrain ¶m,
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const std::vector<float> &grad,
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const std::vector<float> &hess,
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const FMatrix &smat,
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const std::vector<unsigned> &root_index )
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: BaseTreeMaker( tree, param ),
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grad(grad), hess(hess),
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smat(smat), root_index(root_index) {
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utils::Assert( grad.size() == hess.size(), "booster:invalid input" );
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utils::Assert( smat.NumRow() == hess.size(), "booster:invalid input" );
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utils::Assert( root_index.size() == 0 || root_index.size() == hess.size(), "booster:invalid input" );
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}
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inline void Make( int& stat_max_depth, int& stat_num_pruned ){
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this->InitData();
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this->InitNewNode( this->qexpand );
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stat_max_depth = 0;
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for( int depth = 0; depth < param.max_depth; ++ depth ){
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//this->FindSplit( this->qexpand );
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this->UpdateQueueExpand( this->qexpand );
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this->InitNewNode( this->qexpand );
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// if nothing left to be expand, break
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if( qexpand.size() == 0 ) break;
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stat_max_depth = depth + 1;
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}
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// set all the rest expanding nodes to leaf
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for( size_t i = 0; i < qexpand.size(); ++ i ){
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const int nid = qexpand[i];
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tree[ nid ].set_leaf( snode[nid].weight * param.learning_rate );
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}
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// start prunning the tree
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stat_num_pruned = this->DoPrune();
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}
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private:
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// make leaf nodes for all qexpand, update node statistics, mark leaf value
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inline void InitNewNode( const std::vector<int> &qexpand ){
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snode.resize( tree.param.num_nodes, NodeEntry() );
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for( size_t j = 0; j < qexpand.size(); ++ j ){
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const int nid = qexpand[ j ];
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double sum_grad = 0.0, sum_hess = 0.0;
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// TODO: get sum statistics for nid
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// update node statistics
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snode[nid].sum_grad = sum_grad;
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snode[nid].sum_hess = sum_hess;
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snode[nid].root_gain = param.CalcRootGain( sum_grad, sum_hess );
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if( !tree[nid].is_root() ){
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snode[nid].weight = param.CalcWeight( sum_grad, sum_hess, snode[ tree[nid].parent() ].weight );
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}else{
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snode[nid].weight = param.CalcWeight( sum_grad, sum_hess, 0.0f );
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}
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}
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}
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// find splits at current level
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inline void FindSplit( int nid ){
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// TODO
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}
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private:
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// initialize temp data structure
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inline void InitData( void ){
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std::vector<bst_uint> valid_index;
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for( size_t i = 0; i < grad.size(); ++i ){
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if( hess[ i ] < 0.0f ) continue;
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if( param.subsample > 1.0f-1e-6f || random::SampleBinary( param.subsample ) != 0 ){
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valid_index.push_back( static_cast<bst_uint>(i) );
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}
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}
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node_bound.resize( tree.param.num_roots );
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if( root_index.size() == 0 ){
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row_index_set = valid_index;
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// set bound of root node
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node_bound[0] = std::make_pair( 0, (bst_uint)row_index_set.size() );
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}else{
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std::vector<size_t> rptr;
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utils::SparseCSRMBuilder<bst_uint> builder( rptr, row_index_set );
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builder.InitBudget( tree.param.num_roots );
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for( size_t i = 0; i < valid_index.size(); ++i ){
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const bst_uint rid = valid_index[ i ];
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utils::Assert( root_index[ rid ] < (unsigned)tree.param.num_roots, "root id exceed number of roots" );
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builder.AddBudget( root_index[ rid ] );
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}
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builder.InitStorage();
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for( size_t i = 0; i < valid_index.size(); ++i ){
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const bst_uint rid = valid_index[ i ];
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builder.PushElem( root_index[ rid ], rid );
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}
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for( size_t i = 1; i < rptr.size(); ++ i ){
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node_bound[i-1] = std::make_pair( rptr[ i - 1 ], rptr[ i ] );
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}
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}
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{// setup temp space for each thread
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if( param.nthread != 0 ){
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omp_set_num_threads( param.nthread );
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}
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#pragma omp parallel
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{
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this->nthread = omp_get_num_threads();
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}
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snode.reserve( 256 );
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}
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{// expand query
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qexpand.reserve( 256 ); qexpand.clear();
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for( int i = 0; i < tree.param.num_roots; ++ i ){
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qexpand.push_back( i );
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}
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}
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}
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private:
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// number of omp thread used during training
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int nthread;
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// Instance row indexes corresponding to each node
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std::vector<bst_uint> row_index_set;
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// lower and upper bound of each nodes' row_index
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std::vector< std::pair<bst_uint, bst_uint> > node_bound;
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private:
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const std::vector<float> &grad;
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const std::vector<float> &hess;
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const FMatrix &smat;
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const std::vector<unsigned> &root_index;
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};
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};
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};
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#endif
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