cleanup of evaluation metric, move c++11 codes into sample.h for backup, add lambda in a clean way latter
This commit is contained in:
tqchen
parent
cce96e8f41
commit
439d4725a0
@ -13,8 +13,6 @@
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#include "../utils/xgboost_omp.h"
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#include "../utils/xgboost_random.h"
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#include "xgboost_regrank_data.h"
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#include <functional>
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#include <tuple>
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namespace xgboost{
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namespace regrank{
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@ -36,14 +34,17 @@ namespace xgboost{
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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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inline static bool CmpSecond(const std::pair<float, unsigned> &a, const std::pair<float, unsigned> &b){
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return a.second > b.second;
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}
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/*! \brief RMSE */
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struct EvalRMSE : public IEvaluator{
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virtual float Eval(const std::vector<float> &preds,
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const DMatrix::Info &info) const {
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const DMatrix::Info &info) const {
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const unsigned ndata = static_cast<unsigned>(preds.size());
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float sum = 0.0, wsum = 0.0;
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#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
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#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
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for (unsigned i = 0; i < ndata; ++i){
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const float wt = info.GetWeight(i);
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const float diff = info.labels[i] - preds[i];
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@ -60,10 +61,10 @@ namespace xgboost{
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/*! \brief Error */
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struct EvalLogLoss : public IEvaluator{
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virtual float Eval(const std::vector<float> &preds,
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const DMatrix::Info &info) const {
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const DMatrix::Info &info) const {
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const unsigned ndata = static_cast<unsigned>(preds.size());
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float sum = 0.0f, wsum = 0.0f;
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#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
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#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
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for (unsigned i = 0; i < ndata; ++i){
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const float y = info.labels[i];
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const float py = preds[i];
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@ -81,10 +82,10 @@ namespace xgboost{
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/*! \brief Error */
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struct EvalError : public IEvaluator{
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virtual float Eval(const std::vector<float> &preds,
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const DMatrix::Info &info) const {
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const DMatrix::Info &info) const {
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const unsigned ndata = static_cast<unsigned>(preds.size());
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float sum = 0.0f, wsum = 0.0f;
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#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
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#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
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for (unsigned i = 0; i < ndata; ++i){
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const float wt = info.GetWeight(i);
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if (preds[i] > 0.5f){
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@ -112,11 +113,11 @@ namespace xgboost{
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const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
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double sum_auc = 0.0f;
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#pragma omp parallel reduction(+:sum_auc)
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#pragma omp parallel reduction(+:sum_auc)
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{
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// each thread takes a local rec
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std::vector< std::pair<float, unsigned> > rec;
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#pragma omp for schedule(static)
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#pragma omp 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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@ -153,142 +154,109 @@ namespace xgboost{
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}
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};
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/*! \brief Precison at N, for both classification and rank */
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struct EvalPrecision : public IEvaluator{
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unsigned topn_;
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std::string name_;
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EvalPrecision(const char *name){
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name_ = name;
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utils::Assert(sscanf(name, "pre@%u", &topn_));
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}
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/*! \brief Evaluate rank list */
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struct EvalRankList : public IEvaluator{
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public:
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virtual float Eval(const std::vector<float> &preds,
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const DMatrix::Info &info) const {
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const DMatrix::Info &info) const {
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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(), "EvalAuc: group structure must match number of prediction");
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const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
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double sum_pre = 0.0f;
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#pragma omp parallel reduction(+:sum_pre)
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double sum_metric = 0.0f;
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#pragma omp parallel reduction(+:sum_metric)
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{
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// each thread takes a local rec
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std::vector< std::pair<float, unsigned> > rec;
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#pragma omp for schedule(static)
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#pragma omp 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(preds[j], (int)info.labels[j]));
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}
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std::sort(rec.begin(), rec.end(), CmpFirst);
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// calculate Preicsion
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unsigned nhit = 0;
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for (size_t j = 0; j < rec.size() && j < topn_; ++j){
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nhit += rec[j].second;
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}
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sum_pre += ((float)nhit) / topn_;
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sum_metric += this->EvalMetric( rec );
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}
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}
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return static_cast<float>(sum_pre) / ngroup;
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return static_cast<float>(sum_metric) / ngroup;
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}
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virtual const char *Name(void) const{
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return name_.c_str();
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}
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protected:
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EvalRankList(const char *name){
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name_ = name;
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if( sscanf(name, "%*[^@]@%u", &topn_) != 1 ){
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topn_ = UINT_MAX;
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}
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}
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/*! \return evaluation metric, given the pair_sort record, (pred,label) */
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virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &pair_sort ) const = 0;
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protected:
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unsigned topn_;
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std::string name_;
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};
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/*! \brief Normalized DCG */
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class EvalNDCG : public IEvaluator {
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/*! \brief Precison at N, for both classification and rank */
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struct EvalPrecision : public EvalRankList{
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public:
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virtual float Eval(const std::vector<float> &preds,
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const DMatrix::Info &info) const{
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if (info.group_ptr.size() <= 1) return 0;
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float acc = 0;
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std::vector< std::pair<float, float> > pairs_sort;
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for (int i = 0; i < info.group_ptr.size() - 1; i++){
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for (int j = info.group_ptr[i]; j < info.group_ptr[i + 1]; j++){
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pairs_sort.push_back(std::make_pair(preds[j], info.labels[j]));
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}
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acc += NDCG(pairs_sort);
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EvalPrecision(const char *name):EvalRankList(name){}
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protected:
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virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &rec ) const {
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// calculate Preicsion
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std::sort(rec.begin(), rec.end(), CmpFirst);
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unsigned nhit = 0;
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for (size_t j = 0; j < rec.size() && j < this->topn_; ++j){
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nhit += (rec[j].second != 0 );
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}
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return acc / (info.group_ptr.size() - 1);
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}
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static float DCG(const std::vector<float> &labels){
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float ans = 0.0;
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for (int i = 0; i < labels.size(); i++){
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ans += (pow(2, labels[i]) - 1) / log(i + 2);
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}
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return ans;
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}
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virtual const char *Name(void) const {
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return "NDCG";
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}
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private:
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/*\brief Obtain NDCG given the list of labels and predictions
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* \param pairs_sort the first field is prediction and the second is label
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*/
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float NDCG(std::vector< std::pair<float, float> > pairs_sort) const{
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std::sort(pairs_sort.begin(), pairs_sort.end(), [](std::pair<float, float> a, std::pair<float, float> b){
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return std::get<0>(a) > std::get<0>(b);
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});
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float dcg = DCG(pairs_sort);
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std::sort(pairs_sort.begin(), pairs_sort.end(), [](std::pair<float, float> a, std::pair<float, float> b){
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return std::get<1>(a) > std::get<1>(b);
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});
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float IDCG = DCG(pairs_sort);
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if (IDCG == 0) return 0;
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return dcg / IDCG;
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}
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float DCG(std::vector< std::pair<float, float> > pairs_sort) const{
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std::vector<float> labels;
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for (int i = 1; i < pairs_sort.size(); i++){
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labels.push_back(std::get<1>(pairs_sort[i]));
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}
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return DCG(labels);
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return static_cast<float>( nhit ) / topn_;
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}
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};
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/*! \brief Mean Average Precision */
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class EvalMAP : public IEvaluator {
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/*! \brief NDCG */
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struct EvalNDCG : public EvalRankList{
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public:
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virtual float Eval(const std::vector<float> &preds,
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const DMatrix::Info &info) const{
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if (info.group_ptr.size() <= 1) return 0;
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float acc = 0;
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std::vector<std::pair<float,float>> pairs_sort;
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for (int i = 0; i < info.group_ptr.size() - 1; i++){
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for (int j = info.group_ptr[i]; j < info.group_ptr[i + 1]; j++){
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pairs_sort.push_back(std::make_pair(preds[j], info.labels[j]));
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}
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acc += average_precision(pairs_sort);
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}
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return acc / (info.group_ptr.size() - 1);
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}
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virtual const char *Name(void) const {
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return "MAP";
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}
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private:
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/*\brief Obtain average precision given the list of labels and predictions
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* \param pairs_sort the first field is prediction and the second is label
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*/
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float average_precision(std::vector< std::pair<float,float> > pairs_sort) const{
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std::sort(pairs_sort.begin(), pairs_sort.end(), [](std::pair<float, float> a, std::pair<float, float> b){
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return std::get<0>(a) > std::get<0>(b);
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});
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float hits = 0;
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float average_precision = 0;
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for (int j = 0; j < pairs_sort.size(); j++){
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if (std::get<1>(pairs_sort[j]) == 1){
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hits++;
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average_precision += hits / (j + 1);
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EvalNDCG(const char *name):EvalRankList(name){}
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protected:
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inline float CalcDCG( const std::vector< std::pair<float,unsigned> > &rec ) const {
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double sumdcg = 0.0;
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for( size_t i = 0; i < rec.size() && i < this->topn_; i ++ ){
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const unsigned rel = rec[i].second;
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if( rel != 0 ){
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sumdcg += logf( 2.0f ) *((1<<rel)-1) / logf( i + 1 );
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}
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}
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if (hits != 0) average_precision /= hits;
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return average_precision;
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return static_cast<float>(sumdcg);
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}
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virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &rec ) const {
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std::sort(rec.begin(), rec.end(), CmpFirst);
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float idcg = this->CalcDCG(rec);
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std::sort(rec.begin(), rec.end(), CmpSecond);
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float dcg = this->CalcDCG(rec);
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if( idcg == 0.0f ) return 0.0f;
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else return dcg/idcg;
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}
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};
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/*! \brief Precison at N, for both classification and rank */
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struct EvalMAP : public EvalRankList{
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public:
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EvalMAP(const char *name):EvalRankList(name){}
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protected:
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virtual float EvalMetric( std::vector< std::pair<float, unsigned> > &rec ) const {
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std::sort(rec.begin(), rec.end(), CmpFirst);
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unsigned nhits = 0;
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double sumap = 0.0;
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for( size_t i = 0; i < rec.size(); ++i){
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if( rec[i].second != 0 ){
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nhits += 1;
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if( i < this->topn_ ){
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sumap += static_cast<float>(nhits) / (i+1);
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}
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}
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}
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if (nhits != 0) sumap /= nhits;
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return static_cast<float>(sumap);
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}
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};
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};
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@ -306,6 +274,8 @@ namespace xgboost{
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if (!strcmp(name, "logloss")) evals_.push_back(new EvalLogLoss());
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if (!strcmp(name, "auc")) evals_.push_back(new EvalAuc());
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if (!strncmp(name, "pre@", 4)) evals_.push_back(new EvalPrecision(name));
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if (!strncmp(name, "map", 3)) evals_.push_back(new EvalMAP(name));
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if (!strncmp(name, "ndcg", 3)) evals_.push_back(new EvalNDCG(name));
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}
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~EvalSet(){
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for (size_t i = 0; i < evals_.size(); ++i){
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@ -5,10 +5,9 @@
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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 "xgboost_regrank_sample.h"
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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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@ -206,208 +205,5 @@ namespace xgboost{
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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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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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/* \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)
|
||||
+ pow(2, std::get<1>(sorted_triple[index1])) / log(index2 + 2);
|
||||
float ans = (original - changed) / IDCG;
|
||||
if (ans < 0) ans = -ans;
|
||||
return ans;
|
||||
}
|
||||
|
||||
|
||||
inline float GetIDCG(const std::vector< std::tuple<float, float, int> > sorted_triple){
|
||||
std::vector<float> labels;
|
||||
for (int i = 0; i < sorted_triple.size(); i++){
|
||||
labels.push_back(std::get<1>(sorted_triple[i]));
|
||||
}
|
||||
|
||||
std::sort(labels.begin(), labels.end(), std::greater<float>());
|
||||
return EvalNDCG::DCG(labels);
|
||||
}
|
||||
|
||||
inline std::vector< std::tuple<float, float, float, float> > GetMAPAcc(const std::vector< std::tuple<float, float, int> > sorted_triple){
|
||||
std::vector< std::tuple<float, float, float, float> > map_acc;
|
||||
float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
|
||||
for (int i = 0; i < sorted_triple.size(); i++){
|
||||
if (std::get<1>(sorted_triple[i]) == 1) {
|
||||
hit++;
|
||||
acc1 += hit / (i + 1);
|
||||
acc2 += (hit - 1) / (i + 1);
|
||||
acc3 += (hit + 1) / (i + 1);
|
||||
}
|
||||
map_acc.push_back(std::make_tuple(acc1, acc2, acc3, hit));
|
||||
}
|
||||
return map_acc;
|
||||
|
||||
}
|
||||
|
||||
inline void GetGroupGradient(const std::vector<float> &preds,
|
||||
const std::vector<float> &labels,
|
||||
const std::vector<unsigned> &group_index,
|
||||
std::vector<float> &grad,
|
||||
std::vector<float> &hess,
|
||||
const std::vector< std::tuple<float, float, int> > sorted_triple,
|
||||
const std::vector<int> index_remap,
|
||||
const sample::Pairs& pairs,
|
||||
int group){
|
||||
bool j_better;
|
||||
float IDCG, pred_diff, pred_diff_exp, delta;
|
||||
float first_order_gradient, second_order_gradient;
|
||||
std::vector< std::tuple<float, float, float, float> > map_acc;
|
||||
|
||||
if (lambda_ == NDCG){
|
||||
IDCG = GetIDCG(sorted_triple);
|
||||
}
|
||||
else if (lambda_ == MAP){
|
||||
map_acc = GetMAPAcc(sorted_triple);
|
||||
}
|
||||
|
||||
for (int j = group_index[group]; j < group_index[group + 1]; j++){
|
||||
std::vector<int> pair_instance = pairs.GetPairs(j);
|
||||
for (int k = 0; k < pair_instance.size(); k++){
|
||||
j_better = labels[j] > labels[pair_instance[k]];
|
||||
if (j_better){
|
||||
switch (lambda_){
|
||||
case PAIRWISE: delta = 1.0; break;
|
||||
case MAP: delta = GetLambdaMAP(sorted_triple, index_remap[j - group_index[group]], index_remap[pair_instance[k] - group_index[group]], map_acc); break;
|
||||
case NDCG: delta = GetLambdaNDCG(sorted_triple, index_remap[j - group_index[group]], index_remap[pair_instance[k] - group_index[group]], IDCG); break;
|
||||
default: utils::Error("Cannot find the specified loss type");
|
||||
}
|
||||
|
||||
pred_diff = preds[preds[j] - pair_instance[k]];
|
||||
pred_diff_exp = j_better ? expf(-pred_diff) : expf(pred_diff);
|
||||
first_order_gradient = delta * FirstOrderGradient(pred_diff_exp);
|
||||
second_order_gradient = 2 * delta * SecondOrderGradient(pred_diff_exp);
|
||||
hess[j] += second_order_gradient;
|
||||
grad[j] += first_order_gradient;
|
||||
hess[pair_instance[k]] += second_order_gradient;
|
||||
grad[pair_instance[k]] += -first_order_gradient;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/*!
|
||||
* \brief calculate first order gradient of pairwise loss function(f(x) = ln(1+exp(-x)),
|
||||
* given the exponential of the difference of intransformed pair predictions
|
||||
* \param the intransformed prediction of positive instance
|
||||
* \param the intransformed prediction of negative instance
|
||||
* \return first order gradient
|
||||
*/
|
||||
inline float FirstOrderGradient(float pred_diff_exp) const {
|
||||
return -pred_diff_exp / (1 + pred_diff_exp);
|
||||
}
|
||||
|
||||
/*!
|
||||
* \brief calculate second order gradient of pairwise loss function(f(x) = ln(1+exp(-x)),
|
||||
* given the exponential of the difference of intransformed pair predictions
|
||||
* \param the intransformed prediction of positive instance
|
||||
* \param the intransformed prediction of negative instance
|
||||
* \return second order gradient
|
||||
*/
|
||||
inline float SecondOrderGradient(float pred_diff_exp) const {
|
||||
return pred_diff_exp / pow(1 + pred_diff_exp, 2);
|
||||
}
|
||||
|
||||
|
||||
};
|
||||
};
|
||||
};
|
||||
#endif
|
||||
|
||||
@ -125,5 +125,191 @@ namespace xgboost {
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
namespace regrank{
|
||||
// simple pairwise rank
|
||||
class LambdaRankObj : public IObjFunction{
|
||||
public:
|
||||
LambdaRankObj(void){}
|
||||
|
||||
virtual ~LambdaRankObj(){}
|
||||
|
||||
virtual void SetParam(const char *name, const char *val){
|
||||
if (!strcmp("loss_type", name)) loss_.loss_type = atoi(val);
|
||||
if (!strcmp("sampler", name)) sampler_.AssignSampler(atoi(val));
|
||||
if (!strcmp("lambda", name)) lambda_ = atoi(val);
|
||||
}
|
||||
|
||||
virtual void GetGradient(const std::vector<float>& preds,
|
||||
const DMatrix::Info &info,
|
||||
int iter,
|
||||
std::vector<float> &grad,
|
||||
std::vector<float> &hess) {
|
||||
grad.resize(preds.size()); hess.resize(preds.size());
|
||||
const std::vector<unsigned> &group_index = info.group_ptr;
|
||||
utils::Assert(group_index.size() != 0 && group_index.back() == preds.size(), "rank loss must have group file");
|
||||
|
||||
for (int i = 0; i < group_index.size() - 1; i++){
|
||||
sample::Pairs pairs = sampler_.GenPairs(preds, info.labels, group_index[i], group_index[i + 1]);
|
||||
//pairs.GetPairs()
|
||||
std::vector< std::tuple<float, float, int> > sorted_triple = GetSortedTuple(preds, info.labels, group_index, i);
|
||||
std::vector<int> index_remap = GetIndexMap(sorted_triple, group_index[i]);
|
||||
GetGroupGradient(preds, info.labels, group_index,
|
||||
grad, hess, sorted_triple, index_remap, pairs, i);
|
||||
}
|
||||
}
|
||||
|
||||
virtual const char* DefaultEvalMetric(void) {
|
||||
return "auc";
|
||||
}
|
||||
|
||||
private:
|
||||
int lambda_;
|
||||
const static int PAIRWISE = 0;
|
||||
const static int MAP = 1;
|
||||
const static int NDCG = 2;
|
||||
sample::PairSamplerWrapper sampler_;
|
||||
LossType loss_;
|
||||
/* \brief Sorted tuples of a group by the predictions, and
|
||||
* the fields in the return tuples successively are predicions,
|
||||
* labels, and the index of the instance
|
||||
*/
|
||||
inline std::vector< std::tuple<float, float, int> > GetSortedTuple(const std::vector<float> &preds,
|
||||
const std::vector<float> &labels,
|
||||
const std::vector<unsigned> &group_index,
|
||||
int group){
|
||||
std::vector< std::tuple<float, float, int> > sorted_triple;
|
||||
for (int j = group_index[group]; j < group_index[group + 1]; j++){
|
||||
sorted_triple.push_back(std::tuple<float, float, int>(preds[j], labels[j], j));
|
||||
}
|
||||
std::sort(sorted_triple.begin(), sorted_triple.end(),
|
||||
[](std::tuple<float, float, int> a, std::tuple<float, float, int> b){
|
||||
return std::get<0>(a) > std::get<0>(b);
|
||||
});
|
||||
return sorted_triple;
|
||||
}
|
||||
|
||||
inline std::vector<int> GetIndexMap(std::vector< std::tuple<float, float, int> > sorted_triple, int start){
|
||||
std::vector<int> index_remap;
|
||||
index_remap.resize(sorted_triple.size());
|
||||
for (int i = 0; i < sorted_triple.size(); i++){
|
||||
index_remap[std::get<2>(sorted_triple[i]) - start] = i;
|
||||
}
|
||||
return index_remap;
|
||||
}
|
||||
|
||||
inline float GetLambdaMAP(const std::vector< std::tuple<float, float, int> > sorted_triple,
|
||||
int index1, int index2,
|
||||
std::vector< std::tuple<float, float, float, float> > map_acc){
|
||||
if (index1 > index2) std::swap(index1, index2);
|
||||
float original = std::get<0>(map_acc[index2]);
|
||||
if (index1 != 0) original -= std::get<0>(map_acc[index1 - 1]);
|
||||
float changed = 0;
|
||||
if (std::get<1>(sorted_triple[index1]) < std::get<1>(sorted_triple[index2])){
|
||||
changed += std::get<2>(map_acc[index2 - 1]) - std::get<2>(map_acc[index1]);
|
||||
changed += (std::get<3>(map_acc[index1])+ 1.0f) / (index1 + 1);
|
||||
}
|
||||
else{
|
||||
changed += std::get<1>(map_acc[index2 - 1]) - std::get<1>(map_acc[index1]);
|
||||
changed += std::get<3>(map_acc[index2]) / (index2 + 1);
|
||||
}
|
||||
float ans = (changed - original) / (std::get<3>(map_acc[map_acc.size() - 1]));
|
||||
if (ans < 0) ans = -ans;
|
||||
return ans;
|
||||
}
|
||||
|
||||
inline float GetLambdaNDCG(const std::vector< std::tuple<float, float, int> > sorted_triple,
|
||||
int index1,
|
||||
int index2, float IDCG){
|
||||
float original = pow(2, std::get<1>(sorted_triple[index1])) / log(index1 + 2)
|
||||
+ pow(2, std::get<1>(sorted_triple[index2])) / log(index2 + 2);
|
||||
float changed = pow(2, std::get<1>(sorted_triple[index2])) / log(index1 + 2)
|
||||
+ pow(2, std::get<1>(sorted_triple[index1])) / log(index2 + 2);
|
||||
float ans = (original - changed) / IDCG;
|
||||
if (ans < 0) ans = -ans;
|
||||
return ans;
|
||||
}
|
||||
|
||||
|
||||
inline float GetIDCG(const std::vector< std::tuple<float, float, int> > sorted_triple){
|
||||
std::vector<float> labels;
|
||||
for (int i = 0; i < sorted_triple.size(); i++){
|
||||
labels.push_back(std::get<1>(sorted_triple[i]));
|
||||
}
|
||||
|
||||
std::sort(labels.begin(), labels.end(), std::greater<float>());
|
||||
return EvalNDCG::DCG(labels);
|
||||
}
|
||||
|
||||
inline std::vector< std::tuple<float, float, float, float> > GetMAPAcc(const std::vector< std::tuple<float, float, int> > sorted_triple){
|
||||
std::vector< std::tuple<float, float, float, float> > map_acc;
|
||||
float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
|
||||
for (int i = 0; i < sorted_triple.size(); i++){
|
||||
if (std::get<1>(sorted_triple[i]) == 1) {
|
||||
hit++;
|
||||
acc1 += hit / (i + 1);
|
||||
acc2 += (hit - 1) / (i + 1);
|
||||
acc3 += (hit + 1) / (i + 1);
|
||||
}
|
||||
map_acc.push_back(std::make_tuple(acc1, acc2, acc3, hit));
|
||||
}
|
||||
return map_acc;
|
||||
|
||||
}
|
||||
|
||||
inline void GetGroupGradient(const std::vector<float> &preds,
|
||||
const std::vector<float> &labels,
|
||||
const std::vector<unsigned> &group_index,
|
||||
std::vector<float> &grad,
|
||||
std::vector<float> &hess,
|
||||
const std::vector< std::tuple<float, float, int> > sorted_triple,
|
||||
const std::vector<int> index_remap,
|
||||
const sample::Pairs& pairs,
|
||||
int group){
|
||||
bool j_better;
|
||||
float IDCG, pred_diff, pred_diff_exp, delta;
|
||||
float first_order_gradient, second_order_gradient;
|
||||
std::vector< std::tuple<float, float, float, float> > map_acc;
|
||||
|
||||
if (lambda_ == NDCG){
|
||||
IDCG = GetIDCG(sorted_triple);
|
||||
}
|
||||
else if (lambda_ == MAP){
|
||||
map_acc = GetMAPAcc(sorted_triple);
|
||||
}
|
||||
|
||||
for (int j = group_index[group]; j < group_index[group + 1]; j++){
|
||||
std::vector<int> pair_instance = pairs.GetPairs(j);
|
||||
for (int k = 0; k < pair_instance.size(); k++){
|
||||
j_better = labels[j] > labels[pair_instance[k]];
|
||||
if (j_better){
|
||||
switch (lambda_){
|
||||
case PAIRWISE: delta = 1.0; break;
|
||||
case MAP: delta = GetLambdaMAP(sorted_triple, index_remap[j - group_index[group]], index_remap[pair_instance[k] - group_index[group]], map_acc); break;
|
||||
case NDCG: delta = GetLambdaNDCG(sorted_triple, index_remap[j - group_index[group]], index_remap[pair_instance[k] - group_index[group]], IDCG); break;
|
||||
default: utils::Error("Cannot find the specified loss type");
|
||||
}
|
||||
|
||||
pred_diff = preds[preds[j] - pair_instance[k]];
|
||||
pred_diff_exp = j_better ? expf(-pred_diff) : expf(pred_diff);
|
||||
first_order_gradient = delta * FirstOrderGradient(pred_diff_exp);
|
||||
second_order_gradient = 2 * delta * SecondOrderGradient(pred_diff_exp);
|
||||
hess[j] += second_order_gradient;
|
||||
grad[j] += first_order_gradient;
|
||||
hess[pair_instance[k]] += second_order_gradient;
|
||||
grad[pair_instance[k]] += -first_order_gradient;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
inline float FirstOrderGradient(float pred_diff_exp) const {
|
||||
return -pred_diff_exp / (1 + pred_diff_exp);
|
||||
}
|
||||
inline float SecondOrderGradient(float pred_diff_exp) const {
|
||||
return pred_diff_exp / pow(1 + pred_diff_exp, 2);
|
||||
}
|
||||
};
|
||||
|
||||
}
|
||||
#endif
|
||||
#endif
|
||||
|
||||
@ -129,17 +129,15 @@ namespace xgboost{
|
||||
};
|
||||
|
||||
namespace random{
|
||||
/*! \brief random number generator with independent random number seed*/
|
||||
struct Random{
|
||||
/*! \brief set random number seed */
|
||||
inline void Seed( unsigned sd ){
|
||||
this->rseed = sd;
|
||||
}
|
||||
/*! \brief return a real number uniform in [0,1) */
|
||||
inline double RandDouble( void ){
|
||||
|
||||
// return static_cast<double>( rand_( &rseed ) ) / (static_cast<double>( RAND_MAX )+1.0);
|
||||
return static_cast<double>(rand()) / (static_cast<double>(RAND_MAX)+1.0);
|
||||
|
||||
inline double RandDouble( void ){
|
||||
return static_cast<double>( rand_r( &rseed ) ) / (static_cast<double>( RAND_MAX )+1.0);
|
||||
}
|
||||
// random number seed
|
||||
unsigned rseed;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user