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

Merge branch 'dev' of https://github.com/tqchen/xgboost into dev

Browse Source
This commit is contained in:
kalenhaha 2014-05-09 14:07:06 +08:00
commit 0794dd0f6f
7 changed files with 60 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
python/Makefile
View File

@ -1,6 +1,6 @@
export CC = gcc
export CXX = g++
export CFLAGS = -Wall -msse2 -Wno-unknown-pragmas -fopenmp
export CFLAGS = -Wall -O3 -msse2 -Wno-unknown-pragmas -fopenmp
# specify tensor path
SLIB = libxgboostpy.so

11
python/xgboost.py
View File

@ -22,6 +22,13 @@ xglib.XGDMatrixGetLabel.restype = ctypes.POINTER( ctypes.c_float )
xglib.XGDMatrixGetRow.restype = ctypes.POINTER( REntry )
xglib.XGBoosterPredict.restype = ctypes.POINTER( ctypes.c_float )
def ctypes2numpy( cptr, length ):
# convert a ctypes pointer array to numpy
assert isinstance( cptr, ctypes.POINTER( ctypes.c_float ) )
res = numpy.zeros( length, dtype='float32' )
assert ctypes.memmove( res.ctypes.data, cptr, length * res.strides[0] )
return res
# data matrix used in xgboost
class DMatrix:
# constructor
@ -73,7 +80,7 @@ class DMatrix:
def get_label(self):
length = ctypes.c_ulong()
labels = xglib.XGDMatrixGetLabel(self.handle, ctypes.byref(length))
return numpy.array( [labels[i] for i in xrange(length.value)] )
return ctypes2numpy( labels, length.value );
# clear everything
def clear(self):
xglib.XGDMatrixClear(self.handle)
@ -138,7 +145,7 @@ class Booster:
def predict(self, data, bst_group = -1):
length = ctypes.c_ulong()
preds = xglib.XGBoosterPredict( self.handle, data.handle, ctypes.byref(length), bst_group)
return numpy.array( [ preds[i] for i in xrange(length.value)])
return ctypes2numpy( preds, length.value )
def save_model(self, fname):
""" save model to file """
xglib.XGBoosterSaveModel( self.handle, ctypes.c_char_p(fname) )

1
python/xgboost_python.cpp
View File

@ -75,6 +75,7 @@ namespace xgboost{
inline void CheckInit(void){
if(!init_col_){
this->data.InitData();
init_col_ = true;
}
utils::Assert( this->data.NumRow() == this->info.labels.size(), "DMatrix: number of labels must match number of rows in matrix");
}

1
regrank/xgboost_regrank.h
View File

@ -283,6 +283,7 @@ namespace xgboost{
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
preds[j] = mparam.base_score + base_gbm.Predict(data.data, j, buffer_offset + j, data.info.GetRoot(j), bst_group );
}
}else
#pragma omp parallel for schedule( static )

37
regrank/xgboost_regrank_eval.h
View File

@ -83,7 +83,7 @@ namespace xgboost{
float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float wt = info.GetWeight(i);
const float wt = info.GetWeight(i);
if (preds[i] > 0.5f){
if (info.labels[i] < 0.5f) sum += wt;
}
@ -99,6 +99,39 @@ namespace xgboost{
}
};
/*! \brief Error */
struct EvalMatchError : public IEvaluator{
public:
EvalMatchError(const char *name){
name_ = name;
abs_ = 0;
if(!strcmp("mabserror", name)) abs_ =1;
}
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
const unsigned ndata = static_cast<unsigned>(preds.size());
float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float wt = info.GetWeight(i);
int label = static_cast<int>(info.labels[i]);
if( label < 0 && abs_ != 0 ) label = -label-1;
if (static_cast<int>(preds[i]) != label ){
sum += wt;
}
wsum += wt;
}
return sum / wsum;
}
virtual const char *Name(void) const{
return name_.c_str();
}
int abs_;
std::string name_;
};
/*! \brief Area under curve, for both classification and rank */
struct EvalAuc : public IEvaluator{
virtual float Eval(const std::vector<float> &preds,
@ -281,6 +314,8 @@ namespace xgboost{
}
if (!strcmp(name, "rmse")) evals_.push_back(new EvalRMSE());
if (!strcmp(name, "error")) evals_.push_back(new EvalError());
if (!strcmp(name, "merror")) evals_.push_back(new EvalMatchError("merror"));
if (!strcmp(name, "mabserror")) evals_.push_back(new EvalMatchError("mabserror"));
if (!strcmp(name, "logloss")) evals_.push_back(new EvalLogLoss());
if (!strcmp(name, "auc")) evals_.push_back(new EvalAuc());
if (!strncmp(name, "pre@", 4)) evals_.push_back(new EvalPrecision(name));

15
regrank/xgboost_regrank_obj.hpp
View File

@ -77,7 +77,7 @@ namespace xgboost{
#pragma omp parallel
{
std::vector< float > rec;
#pragma for schedule(static)
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k){
rec.clear();
int nhit = 0;
@ -127,13 +127,16 @@ namespace xgboost{
#pragma omp parallel
{
std::vector<float> rec(nclass);
#pragma for schedule(static)
#pragma omp for schedule(static)
for (unsigned j = 0; j < ndata; ++j){
for( int k = 0; k < nclass; ++ k ){
rec[k] = preds[j + k * ndata];
}
Softmax( rec );
int label = static_cast<int>(info.labels[j]);
if( label < 0 ){
label = -label - 1;
}
utils::Assert( label < nclass, "SoftmaxMultiClassObj: label exceed num_class" );
for( int k = 0; k < nclass; ++ k ){
float p = rec[ k ];
@ -151,22 +154,22 @@ namespace xgboost{
utils::Assert( nclass != 0, "must set num_class to use softmax" );
utils::Assert( preds.size() % nclass == 0, "SoftmaxMultiClassObj: label size and pred size does not match" );
const unsigned ndata = static_cast<unsigned>(preds.size()/nclass);
#pragma omp parallel
{
std::vector<float> rec(nclass);
#pragma for schedule(static)
#pragma omp for schedule(static)
for (unsigned j = 0; j < ndata; ++j){
for( int k = 0; k < nclass; ++ k ){
rec[k] = preds[j + k * ndata];
}
Softmax( rec );
preds[j] = FindMaxIndex( rec );
}
}
preds.resize( ndata );
}
virtual const char* DefaultEvalMetric(void) {
return "error";
return "merror";
}
private:
int nclass;
@ -203,7 +206,7 @@ namespace xgboost{
// thread use its own random number generator, seed by thread id and current iteration
random::Random rnd; rnd.Seed( iter * 1111 + omp_get_thread_num() );
std::vector< std::pair<float,unsigned> > rec;
#pragma for schedule(static)
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k){
rec.clear();
for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){

4
regrank/xgboost_regrank_utils.h
View File

@ -26,7 +26,9 @@ namespace xgboost{
inline static int FindMaxIndex( std::vector<float>& rec ){
size_t mxid = 0;
for( size_t i = 1; i < rec.size(); ++ i ){
if( rec[i] > rec[mxid] ) mxid = i;
if( rec[i] > rec[mxid]+1e-6f ){
mxid = i;
}
}
return (int)mxid;
}