Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

big change, change interface to template, everything still OK

Browse Source
This commit is contained in:
tqchen
2014-03-03 22:16:37 -08:00
committed by tqchen
parent a3ca03cfc1
commit b689b4525a
8 changed files with 135 additions and 114 deletions
Download Patch File Download Diff File Expand all files Collapse all files

123
booster/tree/xgboost_tree.hpp
View File

@@ -29,7 +29,8 @@ namespace xgboost{
namespace booster{
// regression tree, construction algorithm is seperated from this class
// see RegTreeUpdater
class RegTreeTrainer : public IBooster{
template<typename FMatrix>
class RegTreeTrainer : public InterfaceBooster<FMatrix>{
public:
RegTreeTrainer( void ){
silent = 0; tree_maker = 1;
@@ -55,62 +56,6 @@ namespace xgboost{
}
public:
virtual void DoBoost( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrixS &smat,
const std::vector<unsigned> &root_index ){
this->DoBoost_( grad, hess, smat, root_index );
}
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 FMatrixS::Line &feat, unsigned gid = 0 ){
path.clear();
ThreadEntry &e = this->InitTmp();
this->PrepareTmp( feat, 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( feat, e );
}
// make it OpenMP thread safe, but not thread safe in general
virtual float Predict( const FMatrixS::Line &feat, unsigned gid = 0 ){
ThreadEntry &e = this->InitTmp();
this->PrepareTmp( feat, e );
int pid = this->GetLeafIndex( e.feat, e.funknown, gid );
this->DropTmp( feat, e );
return tree[ pid ].leaf_value();
}
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:
template<typename FMatrix>
inline void DoBoost_( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &smat,
const std::vector<unsigned> &root_index ){
@@ -131,6 +76,52 @@ namespace xgboost{
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.param.max_depth );
}
}
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:
int silent;
@@ -144,7 +135,6 @@ namespace xgboost{
};
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" );
@@ -156,16 +146,17 @@ namespace xgboost{
}
return e;
}
inline void PrepareTmp( const FMatrixS::Line &feat, ThreadEntry &e ){
for( unsigned i = 0; i < feat.len; i ++ ){
utils::Assert( feat[i].findex < (unsigned)tree.param.num_feature , "input feature execeed bound" );
e.funknown[ feat[i].findex ] = false;
e.feat[ feat[i].findex ] = feat[i].fvalue;
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( const FMatrixS::Line &feat, ThreadEntry &e ){
for( unsigned i = 0; i < feat.len; i ++ ){
e.funknown[ feat[i].findex ] = true;
inline void DropTmp( typename FMatrix::RowIter it, ThreadEntry &e ){
while( it.Next() ){
e.funknown[ it.findex() ] = true;
}
}