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some fix

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This commit is contained in:
tqchen 2014-05-14 16:55:59 -07:00
parent bbe4957cd2
commit 50af92e29e
6 changed files with 43 additions and 16 deletions
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2
python/xgboost.py
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@ -75,7 +75,7 @@ class DMatrix:
xglib.XGDMatrixSetGroup(self.handle, (ctypes.c_uint*len(group))(*group), len(group) )
# set weight of each instances
def set_weight(self, weight):
xglib.XGDMatrixSetWeight(self.handle, (ctypes.c_uint*len(weight))(*weight), len(weight) )
xglib.XGDMatrixSetWeight(self.handle, (ctypes.c_float*len(weight))(*weight), len(weight) )
# get label from dmatrix
def get_label(self):
length = ctypes.c_ulong()

2
python/xgboost_python.cpp
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@ -223,7 +223,7 @@ extern "C"{
mats.push_back( static_cast<DMatrix*>(dmats[i]) );
names.push_back( std::string( evnames[i]) );
}
bst->EvalOneIter( iter, mats, names, stdout );
bst->EvalOneIter( iter, mats, names, stderr );
}
const float *XGBoosterPredict( void *handle, void *dmat, size_t *len, int bst_group ){
return static_cast<Booster*>(handle)->Pred( *static_cast<DMatrix*>(dmat), len, bst_group );

2
regrank/xgboost_regrank.h
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@ -328,7 +328,7 @@ namespace xgboost{
* \brief adjust base_score
*/
inline void AdjustBase(void){
if (loss_type == 1 || loss_type == 2){
if (loss_type == 1 || loss_type == 2|| loss_type == 3){
utils::Assert(base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain");
base_score = -logf(1.0f / base_score - 1.0f);
}

38
regrank/xgboost_regrank_eval.h
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@ -99,26 +99,44 @@ namespace xgboost{
}
};
/*! \brief AMS */
/*! \brief AMS: also records best threshold */
struct EvalAMS : public IEvaluator{
virtual float Eval(const std::vector<float> &preds,
const DMatrix::Info &info) const {
const unsigned ndata = static_cast<unsigned>(preds.size());
double s_tp = 0.0, b_fp = 0.0;
#pragma omp parallel for reduction(+:s_tp,b_fp) schedule( static )
std::vector< std::pair<float, unsigned> > rec(ndata);
#pragma omp parallel for schedule( static )
for (unsigned i = 0; i < ndata; ++i){
const float wt = info.GetWeight(i);
if (preds[i] > 0.5f){
if( info.labels[i] > 0.5f ) s_tp += wt;
else b_fp += wt;
}
}
rec[i] = std::make_pair( preds[i], i );
}
std::sort( rec.begin(), rec.end(), CmpFirst );
const double br = 10.0;
return sqrtf( 2*((s_tp+b_fp+br) * log( 1.0 + s_tp/(b_fp+br) ) - s_tp) );
double s_tp = 0.0, b_fp = 0.0, tams = 0.0, threshold = 0.0;
for (unsigned i = 0; i < ndata-1; ++i){
const unsigned ridx = rec[i].second;
const float wt = info.weights[ridx];
if( info.labels[ridx] > 0.5f ){
s_tp += wt;
}else{
b_fp += wt;
}
if( rec[i].first != rec[i+1].first ){
double ams = sqrtf( 2*((s_tp+b_fp+br) * log( 1.0 + s_tp/(b_fp+br) ) - s_tp) );
if( tams < ams ){
threshold = (rec[i].first + rec[i+1].first) / 2.0;
tams = ams;
}
}
}
fprintf( stderr, "\tams-thres=%g", threshold );
return tams;
}
virtual const char *Name(void) const{
return "ams";
}
double wtarget;
};
/*! \brief Error */

4
regrank/xgboost_regrank_obj.h
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@ -50,6 +50,7 @@ namespace xgboost{
const static int kLinearSquare = 0;
const static int kLogisticNeglik = 1;
const static int kLogisticClassify = 2;
const static int kLogisticRaw = 3;
public:
/*! \brief indicate which type we are using */
int loss_type;
@ -61,6 +62,7 @@ namespace xgboost{
*/
inline float PredTransform(float x){
switch (loss_type){
case kLogisticRaw:
case kLinearSquare: return x;
case kLogisticClassify:
case kLogisticNeglik: return 1.0f / (1.0f + expf(-x));
@ -77,6 +79,7 @@ namespace xgboost{
inline float FirstOrderGradient(float predt, float label) const{
switch (loss_type){
case kLinearSquare: return predt - label;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt - label;
default: utils::Error("unknown loss_type"); return 0.0f;
@ -91,6 +94,7 @@ namespace xgboost{
inline float SecondOrderGradient(float predt, float label) const{
switch (loss_type){
case kLinearSquare: return 1.0f;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt * (1 - predt);
default: utils::Error("unknown loss_type"); return 0.0f;

11
regrank/xgboost_regrank_obj.hpp
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@ -20,6 +20,7 @@ namespace xgboost{
virtual ~RegressionObj(){}
virtual void SetParam(const char *name, const char *val){
if( !strcmp( "loss_type", name ) ) loss.loss_type = atoi( val );
if( !strcmp( "scale_pos_weight", name ) ) scale_pos_weight = (float)atof( val );
}
virtual void GetGradient(const std::vector<float>& preds,
const DMatrix::Info &info,
@ -33,13 +34,16 @@ namespace xgboost{
#pragma omp parallel for schedule( static )
for (unsigned j = 0; j < ndata; ++j){
float p = loss.PredTransform(preds[j]);
grad[j] = loss.FirstOrderGradient(p, info.labels[j]) * info.GetWeight(j);
hess[j] = loss.SecondOrderGradient(p, info.labels[j]) * info.GetWeight(j);
float w = info.GetWeight(j);
if( info.labels[j] == 1.0f ) w *= scale_pos_weight;
grad[j] = loss.FirstOrderGradient(p, info.labels[j]) * w;
hess[j] = loss.SecondOrderGradient(p, info.labels[j]) * w;
}
}
virtual const char* DefaultEvalMetric(void) {
if( loss.loss_type == LossType::kLogisticClassify ) return "error";
else return "rmse";
if( loss.loss_type == LossType::kLogisticRaw ) return "auc";
return "rmse";
}
virtual void PredTransform(std::vector<float> &preds){
const unsigned ndata = static_cast<unsigned>(preds.size());
@ -49,6 +53,7 @@ namespace xgboost{
}
}
private:
float scale_pos_weight;
LossType loss;
};
};