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

ok

Browse Source
This commit is contained in:
tqchen 2014-08-17 20:47:20 -07:00
parent 5a472145de
commit 4ed4b08146
4 changed files with 45 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
src/io/io.cpp
View File

@ -15,7 +15,7 @@ DataMatrix* LoadDataMatrix(const char *fname, bool silent, bool savebuffer) {
}
void SaveDataMatrix(const DataMatrix &dmat, const char *fname, bool silent) {
if (dmat.magic == DMatrixSimple::kMagic){
if (dmat.magic == DMatrixSimple::kMagic) {
const DMatrixSimple *p_dmat = static_cast<const DMatrixSimple*>(&dmat);
p_dmat->SaveBinary(fname, silent);
} else {

2
src/io/io.h
View File

@ -30,7 +30,7 @@ DataMatrix* LoadDataMatrix(const char *fname, bool silent = false, bool savebuff
* \param fname file name to be savd
* \param silent whether print message during saving
*/
void SaveDataMatrix(const DataMatrix &dmat, const char *fname, bool silent = false);
void SaveDataMatrix(const DataMatrix &dmat, const char *fname, bool silent = false);
} // namespace io
} // namespace xgboost

2
src/learner/learner-inl.hpp
View File

@ -203,7 +203,7 @@ class BoostLearner {
inline std::vector<std::string> DumpModel(const utils::FeatMap& fmap, int option) {
return gbm_->DumpModel(fmap, option);
}
protected:
/*!
* \brief initialize the objective function and GBM,

75
src/learner/objective-inl.hpp
View File

@ -8,6 +8,7 @@
#include <vector>
#include <cmath>
#include <algorithm>
#include <utility>
#include <functional>
#include "../data.h"
#include "./objective.h"
@ -254,19 +255,17 @@ class LambdaRankObj : public IObjFunction {
utils::Check(gptr.size() != 0 && gptr.back() == preds.size(),
"group structure not consistent with #rows");
const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
#pragma omp parallel
{
// parall construct, declare random number generator here, so that each
// parall construct, declare random number generator here, so that each
// 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<LambdaPair> pairs;
std::vector<ListEntry> lst;
std::vector< std::pair<float,unsigned> > rec;
std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static)
for (unsigned k = 0; k < ngroup; ++k) {
lst.clear(); pairs.clear();
lst.clear(); pairs.clear();
for (unsigned j = gptr[k]; j < gptr[k+1]; ++j) {
lst.push_back(ListEntry(preds[j], info.labels[j], j));
gpair[j] = bst_gpair(0.0f, 0.0f);
@ -313,8 +312,8 @@ class LambdaRankObj : public IObjFunction {
float g = loss.FirstOrderGradient(p, 1.0f);
float h = loss.SecondOrderGradient(p, 1.0f);
// accumulate gradient and hessian in both pid, and nid
gpair[pos.rindex].grad += g * w;
gpair[pos.rindex].hess += 2.0f * h;
gpair[pos.rindex].grad += g * w;
gpair[pos.rindex].hess += 2.0f * h;
gpair[neg.rindex].grad -= g * w;
gpair[neg.rindex].hess += 2.0f * h;
}
@ -332,7 +331,7 @@ class LambdaRankObj : public IObjFunction {
float pred;
/*! \brief the actual label of the entry */
float label;
/*! \brief row index in the data matrix */
/*! \brief row index in the data matrix */
unsigned rindex;
// constructor
ListEntry(float pred, float label, unsigned rindex)
@ -370,14 +369,14 @@ class LambdaRankObj : public IObjFunction {
// loss function
LossType loss;
// number of samples peformed for each instance
int num_pairsample;
int num_pairsample;
// fix weight of each elements in list
float fix_list_weight;
};
class PairwiseRankObj: public LambdaRankObj{
public:
virtual ~PairwiseRankObj(void){}
virtual ~PairwiseRankObj(void) {}
protected:
virtual void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
@ -402,7 +401,6 @@ class LambdaRankObjNDCG : public LambdaRankObj {
std::sort(labels.begin(), labels.end(), std::greater<float>());
IDCG = CalcDCG(labels);
}
if (IDCG == 0.0) {
for (size_t i = 0; i < pairs.size(); ++i) {
pairs[i].weight = 0.0f;
@ -412,13 +410,15 @@ class LambdaRankObjNDCG : public LambdaRankObj {
for (size_t i = 0; i < pairs.size(); ++i) {
unsigned pos_idx = pairs[i].pos_index;
unsigned neg_idx = pairs[i].neg_index;
float pos_loginv = 1.0f / logf(pos_idx+2.0f);
float neg_loginv = 1.0f / logf(neg_idx+2.0f);
float pos_loginv = 1.0f / logf(pos_idx + 2.0f);
float neg_loginv = 1.0f / logf(neg_idx + 2.0f);
int pos_label = static_cast<int>(sorted_list[pos_idx].label);
int neg_label = static_cast<int>(sorted_list[neg_idx].label);
float original = ((1<<pos_label)-1) * pos_loginv + ((1<<neg_label)-1) * neg_loginv;
float changed = ((1<<neg_label)-1) * pos_loginv + ((1<<pos_label)-1) * neg_loginv;
float delta = (original-changed) * IDCG;
float original =
((1 << pos_label) - 1) * pos_loginv + ((1 << neg_label) - 1) * neg_loginv;
float changed =
((1 << neg_label) - 1) * pos_loginv + ((1 << pos_label) - 1) * neg_loginv;
float delta = (original - changed) * IDCG;
if (delta < 0.0f) delta = - delta;
pairs[i].weight = delta;
}
@ -428,25 +428,31 @@ class LambdaRankObjNDCG : public LambdaRankObj {
double sumdcg = 0.0;
for (size_t i = 0; i < labels.size(); ++i) {
const unsigned rel = labels[i];
if (rel != 0) {
sumdcg += ((1<<rel)-1) / logf(i + 2);
if (rel != 0) {
sumdcg += ((1 << rel) - 1) / logf(i + 2);
}
}
return static_cast<float>(sumdcg);
}
};
class LambdaRankObjMAP : public LambdaRankObj {
class LambdaRankObjMAP : public LambdaRankObj {
public:
virtual ~LambdaRankObjMAP(void) {}
protected:
struct MAPStats {
/* \brief the accumulated precision */
/*! \brief the accumulated precision */
float ap_acc;
/* \brief the accumulated precision assuming a positive instance is missing */
/*!
* \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*/
/*!
* \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;
@ -454,7 +460,7 @@ class LambdaRankObjMAP : public LambdaRankObj {
MAPStats(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_list the list containing entry information
@ -463,7 +469,8 @@ class LambdaRankObjMAP : public LambdaRankObj {
*/
inline float GetLambdaMAP(const std::vector<ListEntry> &sorted_list,
int index1, int index2,
std::vector<MAPStats> &map_stats){
std::vector<MAPStats> *p_map_stats) {
std::vector<MAPStats> &map_stats = *p_map_stats;
if (index1 == index2 || map_stats[map_stats.size() - 1].hits == 0) {
return 0.0f;
}
@ -482,18 +489,18 @@ class LambdaRankObjMAP : public LambdaRankObj {
changed += map_stats[index2 - 1].ap_acc_miss - map_stats[index1].ap_acc_miss;
changed += map_stats[index2].hits / (index2 + 1);
}
float ans = (changed - original) / (map_stats[map_stats.size() - 1].hits);
if (ans < 0) ans = -ans;
return ans;
}
}
/*
* \brief obtain preprocessing results for calculating delta MAP
* \param sorted_list the list containing entry information
* \param map_stats a vector containing the accumulated precisions for each position in a list
*/
inline void GetMAPStats(const std::vector<ListEntry> &sorted_list,
std::vector<MAPStats> &map_acc){
std::vector<MAPStats> *p_map_acc) {
std::vector<MAPStats> &map_acc = *p_map_acc;
map_acc.resize(sorted_list.size());
float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
for (size_t i = 1; i <= sorted_list.size(); ++i) {
@ -503,16 +510,18 @@ class LambdaRankObjMAP : public LambdaRankObj {
acc2 += (hit - 1) / i;
acc3 += (hit + 1) / i;
}
map_acc[i - 1] = MAPStats(acc1,acc2,acc3,hit);
map_acc[i - 1] = MAPStats(acc1, acc2, acc3, hit);
}
}
virtual void GetLambdaWeight(const std::vector<ListEntry> &sorted_list, std::vector<LambdaPair> *io_pairs) {
}
virtual void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
std::vector<LambdaPair> *io_pairs) {
std::vector<LambdaPair> &pairs = *io_pairs;
std::vector<MAPStats> map_stats;
GetMAPStats(sorted_list, map_stats);
GetMAPStats(sorted_list, &map_stats);
for (size_t i = 0; i < pairs.size(); ++i) {
pairs[i].weight =
GetLambdaMAP(sorted_list, pairs[i].pos_index, pairs[i].neg_index, map_stats);
pairs[i].weight =
GetLambdaMAP(sorted_list, pairs[i].pos_index,
pairs[i].neg_index, &map_stats);
}
}
};