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

Browse Source 
Conflicts:
	demo/rank/mq2008.conf
	demo/rank/runexp.sh
	regrank/xgboost_regrank_obj.h
This commit is contained in:
yepyao
2014-05-17 10:40:12 +08:00
parent b19f2bfda8 21b21e69de
commit da482500c7
21 changed files with 126 additions and 191 deletions
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2
regrank/xgboost_regrank.h
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@@ -25,7 +25,7 @@ namespace xgboost{
RegRankBoostLearner(void){
silent = 0;
obj_ = NULL;
name_obj_ = "reg";
name_obj_ = "reg:linear";
}
/*!
* \brief a regression booter associated with training and evaluating data

4
regrank/xgboost_regrank_data.h
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@@ -129,7 +129,9 @@ namespace xgboost{
if( fs.Read(&nwt, sizeof(unsigned) ) != 0 ){
utils::Assert( nwt == 0 || nwt == data.NumRow(), "invalid weight" );
info.weights.resize( nwt );
utils::Assert( fs.Read(&info.weights[0], sizeof(unsigned) * nwt) != 0, "Load weight file");
if( nwt != 0 ){
utils::Assert( fs.Read(&info.weights[0], sizeof(unsigned) * nwt) != 0, "Load weight file");
}
}
}
fs.Close();

164
regrank/xgboost_regrank_dev.hpp
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@@ -1,164 +0,0 @@
// some backup code
class LambdaRankObj_NDCG : public LambdaRankObj{
static inline float CalcDCG(const std::vector< float > &rec) {
double sumdcg = 0.0;
for (size_t i = 0; i < rec.size(); i++){
const unsigned rel = static_cast<unsigned>(rec[i]);
if (rel != 0){
sumdcg += logf(2.0f) *((1 << rel) - 1) / logf(i + 2);
}
}
return static_cast<float>(sumdcg);
}
/*
* \brief Obtain the delta NDCG if trying to switch the positions of instances in index1 or index2
* in sorted triples. Here DCG is calculated as sigma_i 2^rel_i/log(i + 1)
* \param sorted_triple the fields are predition,label,original index
* \param index1,index2 the instances switched
* \param the IDCG of the list
*/
inline float GetLambdaNDCG(const std::vector< Triple > sorted_triple,
int index1,
int index2, float IDCG){
double original = (1 << static_cast<int>(sorted_triple[index1].label_)) / log(index1 + 2)
+ (1 << static_cast<int>(sorted_triple[index2].label_)) / log(index2 + 2);
double changed = (1 << static_cast<int>(sorted_triple[index2].label_)) / log(index1 + 2)
+ (1 << static_cast<int>(sorted_triple[index1].label_)) / log(index2 + 2);
double ans = (original - changed) / IDCG;
if (ans < 0) ans = -ans;
return static_cast<float>(ans);
}
inline float GetIDCG(const std::vector< Triple > sorted_triple){
std::vector<float> labels;
for (size_t i = 0; i < sorted_triple.size(); i++){
labels.push_back(sorted_triple[i].label_);
}
std::sort(labels.begin(), labels.end(), std::greater<float>());
return CalcDCG(labels);
}
inline void GetLambda(const std::vector<float> &preds,
const std::vector<float> &labels,
const std::vector<unsigned> &group_index,
const std::vector< std::pair<int, int> > &pairs, std::vector<float> &lambda, int group){
std::vector< Triple > sorted_triple;
std::vector<int> index_remap;
float IDCG;
GetSortedTuple(preds, labels, group_index, group, sorted_triple);
GetIndexMap(sorted_triple, group_index[group], index_remap);
IDCG = GetIDCG(sorted_triple);
lambda.resize(pairs.size());
for (size_t i = 0; i < pairs.size(); i++){
lambda[i] = GetLambdaNDCG(sorted_triple,
index_remap[pairs[i].first],index_remap[pairs[i].second],IDCG);
}
}
};
class LambdaRankObj_MAP : public LambdaRankObj{
class Quadruple{
public:
/* \brief the accumulated precision */
float ap_acc_;
/* \brief the accumulated precision assuming a positive instance is missing*/
float ap_acc_miss_;
/* \brief the accumulated precision assuming that one more positive instance is inserted ahead*/
float ap_acc_add_;
/* \brief the accumulated positive instance count */
float hits_;
Quadruple(){}
Quadruple(const Quadruple& q){
ap_acc_ = q.ap_acc_;
ap_acc_miss_ = q.ap_acc_miss_;
ap_acc_add_ = q.ap_acc_add_;
hits_ = q.hits_;
}
Quadruple(float ap_acc, float ap_acc_miss, float ap_acc_add, float hits
) :ap_acc_(ap_acc), ap_acc_miss_(ap_acc_miss), ap_acc_add_(ap_acc_add), hits_(hits){
}
};
/*
* \brief Obtain the delta MAP if trying to switch the positions of instances in index1 or index2
* in sorted triples
* \param sorted_triple the fields are predition,label,original index
* \param index1,index2 the instances switched
* \param map_acc a vector containing the accumulated precisions for each position in a list
*/
inline float GetLambdaMAP(const std::vector< Triple > sorted_triple,
int index1, int index2,
std::vector< Quadruple > &map_acc){
if (index1 == index2 || sorted_triple[index1].label_ == sorted_triple[index2].label_) return 0.0;
if (index1 > index2) std::swap(index1, index2);
float original = map_acc[index2].ap_acc_; // The accumulated precision in the interval [index1,index2]
if (index1 != 0) original -= map_acc[index1 - 1].ap_acc_;
float changed = 0;
if (sorted_triple[index1].label_ < sorted_triple[index2].label_){
changed += map_acc[index2 - 1].ap_acc_add_ - map_acc[index1].ap_acc_add_;
changed += (map_acc[index1].hits_ + 1.0f) / (index1 + 1);
}
else{
changed += map_acc[index2 - 1].ap_acc_miss_ - map_acc[index1].ap_acc_miss_;
changed += map_acc[index2].hits_ / (index2 + 1);
}
float ans = (changed - original) / (map_acc[map_acc.size() - 1].hits_);
if (ans < 0) ans = -ans;
return ans;
}
/*
* \brief preprocessing results for calculating delta MAP
* \return The first field is the accumulated precision, the second field is the
* accumulated precision assuming a positive instance is missing,
* the third field is the accumulated precision assuming that one more positive
* instance is inserted, the fourth field is the accumulated positive instance count
*/
inline void GetMAPAcc(const std::vector< Triple > sorted_triple,
std::vector< Quadruple > &map_acc){
map_acc.resize(sorted_triple.size());
float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
for (size_t i = 1; i <= sorted_triple.size(); i++){
if ((int)sorted_triple[i - 1].label_ == 1) {
hit++;
acc1 += hit / i;
acc2 += (hit - 1) / i;
acc3 += (hit + 1) / i;
}
map_acc[i-1] = Quadruple(acc1, acc2, acc3, hit);
}
}
inline void GetLambda(const std::vector<float> &preds,
const std::vector<float> &labels,
const std::vector<unsigned> &group_index,
const std::vector< std::pair<int, int> > &pairs, std::vector<float> &lambda, int group){
std::vector< Triple > sorted_triple;
std::vector<int> index_remap;
std::vector< Quadruple > map_acc;
GetSortedTuple(preds, labels, group_index, group, sorted_triple);
GetIndexMap(sorted_triple, group_index[group], index_remap);
GetMAPAcc(sorted_triple, map_acc);
lambda.resize(pairs.size());
for (size_t i = 0; i < pairs.size(); i++){
lambda[i] = GetLambdaMAP(sorted_triple,
index_remap[pairs[i].first], index_remap[pairs[i].second], map_acc);
}
}
};

13
regrank/xgboost_regrank_obj.h
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@@ -109,6 +109,7 @@ namespace xgboost{
namespace xgboost{
namespace regrank{
inline IObjFunction* CreateObjFunction( const char *name ){
<<<<<<< HEAD
if( !strcmp("reg", name ) ) return new RegressionObj();
if( !strcmp("rank:pairwise", name ) ) return new PairwiseRankObj();
if( !strcmp("rank:softmax", name ) ) return new SoftmaxRankObj();
@@ -118,6 +119,18 @@ namespace xgboost{
utils::Error("unknown objective function type");
return NULL;
}
=======
if( !strcmp("reg:linear", name ) ) return new RegressionObj( LossType::kLinearSquare );
if( !strcmp("reg:logistic", name ) ) return new RegressionObj( LossType::kLogisticNeglik );
if( !strcmp("binary:logistic", name ) ) return new RegressionObj( LossType::kLogisticClassify );
if( !strcmp("binary:logitraw", name ) ) return new RegressionObj( LossType::kLogisticRaw );
if( !strcmp("multi:softmax", name ) ) return new SoftmaxMultiClassObj();
if( !strcmp("rank:pairwise", name ) ) return new PairwiseRankObj();
if( !strcmp("rank:softmax", name ) ) return new SoftmaxRankObj();
utils::Error("unknown objective function type");
return NULL;
}
>>>>>>> 9eabb5c7f912a326005aca53a76c2e53a1661842
};
};
#endif

4
regrank/xgboost_regrank_obj.hpp
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@@ -14,8 +14,8 @@ namespace xgboost{
namespace regrank{
class RegressionObj : public IObjFunction{
public:
RegressionObj(void){
loss.loss_type = LossType::kLinearSquare;
RegressionObj( int loss_type ){
loss.loss_type = loss_type;
}
virtual ~RegressionObj(){}
virtual void SetParam(const char *name, const char *val){