414 lines
20 KiB
C++
414 lines
20 KiB
C++
#ifndef XGBOOST_REGRANK_OBJ_HPP
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#define XGBOOST_REGRANK_OBJ_HPP
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/*!
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* \file xgboost_regrank_obj.h
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* \brief implementation of objective functions
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* \author Tianqi Chen, Kailong Chen
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*/
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#include "xgboost_regrank_sample.h"
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#include <tuple>
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#include <vector>
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#include <functional>
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namespace xgboost{
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namespace regrank{
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class RegressionObj : public IObjFunction{
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public:
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RegressionObj(void){
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loss.loss_type = LossType::kLinearSquare;
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}
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virtual ~RegressionObj(){}
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virtual void SetParam(const char *name, const char *val){
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if( !strcmp( "loss_type", name ) ) loss.loss_type = atoi( val );
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}
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virtual void GetGradient(const std::vector<float>& preds,
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const DMatrix::Info &info,
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int iter,
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std::vector<float> &grad,
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std::vector<float> &hess ) {
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grad.resize(preds.size()); hess.resize(preds.size());
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const unsigned ndata = static_cast<unsigned>(preds.size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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float p = loss.PredTransform(preds[j]);
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grad[j] = loss.FirstOrderGradient(p, info.labels[j]) * info.GetWeight(j);
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hess[j] = loss.SecondOrderGradient(p, info.labels[j]) * info.GetWeight(j);
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}
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}
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virtual const char* DefaultEvalMetric(void) {
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if( loss.loss_type == LossType::kLogisticClassify ) return "error";
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else return "rmse";
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}
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virtual void PredTransform(std::vector<float> &preds){
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const unsigned ndata = static_cast<unsigned>(preds.size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = loss.PredTransform( preds[j] );
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}
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}
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private:
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LossType loss;
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};
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};
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namespace regrank{
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// simple softmax rak
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class SoftmaxObj : public IObjFunction{
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public:
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SoftmaxObj(void){
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}
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virtual ~SoftmaxObj(){}
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virtual void SetParam(const char *name, const char *val){
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}
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virtual void GetGradient(const std::vector<float>& preds,
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const DMatrix::Info &info,
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int iter,
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std::vector<float> &grad,
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std::vector<float> &hess ) {
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grad.resize(preds.size()); hess.resize(preds.size());
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const std::vector<unsigned> &gptr = info.group_ptr;
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utils::Assert( gptr.size() != 0 && gptr.back() == preds.size(), "rank loss must have group file" );
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const unsigned ngroup = static_cast<unsigned>( gptr.size() - 1 );
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#pragma omp parallel
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{
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std::vector< float > rec;
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#pragma for schedule(static)
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for (unsigned k = 0; k < ngroup; ++k){
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rec.clear();
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int nhit = 0;
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for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
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rec.push_back( preds[j] );
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grad[j] = hess[j] = 0.0f;
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nhit += info.labels[j];
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}
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Softmax( rec );
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if( nhit == 1 ){
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for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
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float p = rec[ j - gptr[k] ];
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grad[j] = p - info.labels[j];
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hess[j] = 2.0f * p * ( 1.0f - p );
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}
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}else{
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utils::Assert( nhit == 0, "softmax does not allow multiple labels" );
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}
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}
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}
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}
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virtual const char* DefaultEvalMetric(void) {
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return "pre@1";
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}
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private:
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inline static void Softmax( std::vector<float>& rec ){
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float wmax = rec[0];
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for( size_t i = 1; i < rec.size(); ++ i ){
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wmax = std::max( rec[i], wmax );
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}
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double wsum = 0.0f;
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for( size_t i = 0; i < rec.size(); ++ i ){
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rec[i] = expf(rec[i]-wmax);
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wsum += rec[i];
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}
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for( size_t i = 0; i < rec.size(); ++ i ){
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rec[i] /= wsum;
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}
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}
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};
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};
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namespace regrank{
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// simple pairwise rank
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class PairwiseRankObj : public IObjFunction{
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public:
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PairwiseRankObj(void){
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loss.loss_type = LossType::kLinearSquare;
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fix_list_weight = 0.0f;
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}
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virtual ~PairwiseRankObj(){}
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virtual void SetParam(const char *name, const char *val){
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if( !strcmp( "loss_type", name ) ) loss.loss_type = atoi( val );
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if( !strcmp( "fix_list_weight", name ) ) fix_list_weight = (float)atof( val );
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}
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virtual void GetGradient(const std::vector<float>& preds,
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const DMatrix::Info &info,
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int iter,
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std::vector<float> &grad,
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std::vector<float> &hess ) {
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grad.resize(preds.size()); hess.resize(preds.size());
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const std::vector<unsigned> &gptr = info.group_ptr;
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utils::Assert( gptr.size() != 0 && gptr.back() == preds.size(), "rank loss must have group file" );
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const unsigned ngroup = static_cast<unsigned>( gptr.size() - 1 );
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#pragma omp parallel
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{
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// parall construct, declare random number generator here, so that each
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// thread use its own random number generator, seed by thread id and current iteration
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random::Random rnd; rnd.Seed( iter * 1111 + omp_get_thread_num() );
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std::vector< std::pair<float,unsigned> > rec;
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#pragma for schedule(static)
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for (unsigned k = 0; k < ngroup; ++k){
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rec.clear();
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for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
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rec.push_back( std::make_pair(info.labels[j], j) );
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grad[j] = hess[j] = 0.0f;
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}
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std::sort( rec.begin(), rec.end(), CmpFirst );
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// enumerate buckets with same label, for each item in the list, grab another sample randomly
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for( unsigned i = 0; i < rec.size(); ){
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unsigned j = i + 1;
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while( j < rec.size() && rec[j].first == rec[i].first ) ++ j;
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// bucket in [i,j), get a sample outside bucket
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unsigned nleft = i, nright = rec.size() - j;
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for( unsigned pid = i; pid < j; ++ pid ){
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unsigned ridx = static_cast<int>( rnd.RandDouble() * (nleft+nright) );
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if( ridx < nleft ){
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// get the samples in left side, ridx is pos sample
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this->AddGradient( rec[ridx].second, rec[pid].second, preds, grad, hess );
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}else{
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// get samples in right side, ridx is negsample
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this->AddGradient( rec[pid].second, rec[ridx+j-i].second, preds, grad, hess );
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}
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}
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i = j;
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}
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// rescale each gradient and hessian so that the list have constant weight
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if( fix_list_weight != 0.0f ){
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float scale = fix_list_weight / (gptr[k+1] - gptr[k]);
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for(unsigned j = gptr[k]; j < gptr[k+1]; ++j ){
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grad[j] *= scale; hess[j] *= scale;
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}
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}
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}
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}
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}
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virtual const char* DefaultEvalMetric(void) {
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return "auc";
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}
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private:
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inline void AddGradient( unsigned pid, unsigned nid,
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const std::vector<float> &pred,
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std::vector<float> &grad,
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std::vector<float> &hess ){
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float p = loss.PredTransform( pred[pid]-pred[nid] );
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float g = loss.FirstOrderGradient( p, 1.0f );
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float h = loss.SecondOrderGradient( p, 1.0f );
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// accumulate gradient and hessian in both pid, and nid,
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grad[pid] += g; grad[nid] -= g;
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// take conservative update, scale hessian by 2
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hess[pid] += 2.0f * h; hess[nid] += 2.0f * h;
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}
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inline static bool CmpFirst( const std::pair<float,unsigned> &a, const std::pair<float,unsigned> &b ){
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return a.first > b.first;
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}
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private:
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// fix weight of each list
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float fix_list_weight;
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LossType loss;
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};
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};
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namespace regrank{
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// simple pairwise rank
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class LambdaRankObj : public IObjFunction{
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public:
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LambdaRankObj(void){}
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virtual ~LambdaRankObj(){}
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virtual void SetParam(const char *name, const char *val){
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if (!strcmp("loss_type", name)) loss_.loss_type = atoi(val);
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if (!strcmp("sampler", name)) sampler_.AssignSampler(atoi(val));
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if (!strcmp("lambda", name)) lambda_ = atoi(val);
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}
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virtual void GetGradient(const std::vector<float>& preds,
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const DMatrix::Info &info,
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int iter,
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std::vector<float> &grad,
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std::vector<float> &hess) {
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grad.resize(preds.size()); hess.resize(preds.size());
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const std::vector<unsigned> &group_index = info.group_ptr;
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utils::Assert(group_index.size() != 0 && group_index.back() == preds.size(), "rank loss must have group file");
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for (int i = 0; i < group_index.size() - 1; i++){
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sample::Pairs pairs = sampler_.GenPairs(preds, info.labels, group_index[i], group_index[i + 1]);
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//pairs.GetPairs()
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std::vector< std::tuple<float, float, int> > sorted_triple = GetSortedTuple(preds, info.labels, group_index, i);
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std::vector<int> index_remap = GetIndexMap(sorted_triple, group_index[i]);
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GetGroupGradient(preds, info.labels, group_index,
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grad, hess, sorted_triple, index_remap, pairs, i);
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}
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}
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virtual const char* DefaultEvalMetric(void) {
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return "auc";
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}
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private:
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/* \brief Sorted tuples of a group by the predictions, and
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* the fields in the return tuples successively are predicions,
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* labels, and the index of the instance
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*/
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inline std::vector< std::tuple<float, float, int> > GetSortedTuple(const std::vector<float> &preds,
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const std::vector<float> &labels,
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const std::vector<unsigned> &group_index,
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int group){
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std::vector< std::tuple<float, float, int> > sorted_triple;
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for (int j = group_index[group]; j < group_index[group + 1]; j++){
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sorted_triple.push_back(std::tuple<float, float, int>(preds[j], labels[j], j));
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}
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std::sort(sorted_triple.begin(), sorted_triple.end(),
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[](std::tuple<float, float, int> a, std::tuple<float, float, int> b){
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return std::get<0>(a) > std::get<0>(b);
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});
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return sorted_triple;
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}
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inline std::vector<int> GetIndexMap(std::vector< std::tuple<float, float, int> > sorted_triple, int start){
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std::vector<int> index_remap;
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index_remap.resize(sorted_triple.size());
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for (int i = 0; i < sorted_triple.size(); i++){
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index_remap[std::get<2>(sorted_triple[i]) - start] = i;
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}
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return index_remap;
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}
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inline float GetLambdaMAP(const std::vector< std::tuple<float, float, int> > sorted_triple,
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int index1, int index2,
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std::vector< std::tuple<float, float, float, float> > map_acc){
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if (index1 > index2) std::swap(index1, index2);
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float original = std::get<0>(map_acc[index2]);
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if (index1 != 0) original -= std::get<0>(map_acc[index1 - 1]);
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float changed = 0;
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if (std::get<1>(sorted_triple[index1]) < std::get<1>(sorted_triple[index2])){
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changed += std::get<2>(map_acc[index2 - 1]) - std::get<2>(map_acc[index1]);
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changed += (std::get<3>(map_acc[index1])+ 1.0f) / (index1 + 1);
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}
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else{
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changed += std::get<1>(map_acc[index2 - 1]) - std::get<1>(map_acc[index1]);
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changed += std::get<3>(map_acc[index2]) / (index2 + 1);
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}
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float ans = (changed - original) / (std::get<3>(map_acc[map_acc.size() - 1]));
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if (ans < 0) ans = -ans;
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return ans;
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}
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inline float GetLambdaNDCG(const std::vector< std::tuple<float, float, int> > sorted_triple,
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int index1,
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int index2, float IDCG){
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float original = pow(2, std::get<1>(sorted_triple[index1])) / log(index1 + 2)
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+ pow(2, std::get<1>(sorted_triple[index2])) / log(index2 + 2);
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float changed = pow(2, std::get<1>(sorted_triple[index2])) / log(index1 + 2)
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+ pow(2, std::get<1>(sorted_triple[index1])) / log(index2 + 2);
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float ans = (original - changed) / IDCG;
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if (ans < 0) ans = -ans;
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return ans;
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}
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inline float GetIDCG(const std::vector< std::tuple<float, float, int> > sorted_triple){
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std::vector<float> labels;
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for (int i = 0; i < sorted_triple.size(); i++){
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labels.push_back(std::get<1>(sorted_triple[i]));
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}
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std::sort(labels.begin(), labels.end(), std::greater<float>());
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return EvalNDCG::DCG(labels);
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}
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inline std::vector< std::tuple<float, float, float, float> > GetMAPAcc(const std::vector< std::tuple<float, float, int> > sorted_triple){
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std::vector< std::tuple<float, float, float, float> > map_acc;
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float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
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for (int i = 0; i < sorted_triple.size(); i++){
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if (std::get<1>(sorted_triple[i]) == 1) {
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hit++;
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acc1 += hit / (i + 1);
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acc2 += (hit - 1) / (i + 1);
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acc3 += (hit + 1) / (i + 1);
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}
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map_acc.push_back(std::make_tuple(acc1, acc2, acc3, hit));
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}
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return map_acc;
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}
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inline void GetGroupGradient(const std::vector<float> &preds,
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const std::vector<float> &labels,
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const std::vector<unsigned> &group_index,
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std::vector<float> &grad,
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std::vector<float> &hess,
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const std::vector< std::tuple<float, float, int> > sorted_triple,
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const std::vector<int> index_remap,
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const sample::Pairs& pairs,
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int group){
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bool j_better;
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float IDCG, pred_diff, pred_diff_exp, delta;
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float first_order_gradient, second_order_gradient;
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std::vector< std::tuple<float, float, float, float> > map_acc;
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if (lambda_ == NDCG){
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IDCG = GetIDCG(sorted_triple);
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}
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else if (lambda_ == MAP){
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map_acc = GetMAPAcc(sorted_triple);
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}
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for (int j = group_index[group]; j < group_index[group + 1]; j++){
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std::vector<int> pair_instance = pairs.GetPairs(j);
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for (int k = 0; k < pair_instance.size(); k++){
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j_better = labels[j] > labels[pair_instance[k]];
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if (j_better){
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switch (lambda_){
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case PAIRWISE: delta = 1.0; break;
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case MAP: delta = GetLambdaMAP(sorted_triple, index_remap[j - group_index[group]], index_remap[pair_instance[k] - group_index[group]], map_acc); break;
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case NDCG: delta = GetLambdaNDCG(sorted_triple, index_remap[j - group_index[group]], index_remap[pair_instance[k] - group_index[group]], IDCG); break;
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default: utils::Error("Cannot find the specified loss type");
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}
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pred_diff = preds[preds[j] - pair_instance[k]];
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pred_diff_exp = j_better ? expf(-pred_diff) : expf(pred_diff);
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first_order_gradient = delta * FirstOrderGradient(pred_diff_exp);
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second_order_gradient = 2 * delta * SecondOrderGradient(pred_diff_exp);
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hess[j] += second_order_gradient;
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grad[j] += first_order_gradient;
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hess[pair_instance[k]] += second_order_gradient;
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grad[pair_instance[k]] += -first_order_gradient;
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}
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}
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}
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}
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/*!
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* \brief calculate first order gradient of pairwise loss function(f(x) = ln(1+exp(-x)),
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* given the exponential of the difference of intransformed pair predictions
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* \param the intransformed prediction of positive instance
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* \param the intransformed prediction of negative instance
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* \return first order gradient
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*/
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inline float FirstOrderGradient(float pred_diff_exp) const {
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return -pred_diff_exp / (1 + pred_diff_exp);
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}
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/*!
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* \brief calculate second order gradient of pairwise loss function(f(x) = ln(1+exp(-x)),
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* given the exponential of the difference of intransformed pair predictions
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* \param the intransformed prediction of positive instance
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* \param the intransformed prediction of negative instance
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* \return second order gradient
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*/
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inline float SecondOrderGradient(float pred_diff_exp) const {
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return pred_diff_exp / pow(1 + pred_diff_exp, 2);
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}
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private:
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int lambda_;
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const static int PAIRWISE = 0;
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const static int MAP = 1;
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const static int NDCG = 2;
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sample::PairSamplerWrapper sampler_;
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LossType loss_;
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};
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};
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};
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#endif
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