lambda rank added
This commit is contained in:
kalenhaha
parent
bf64608cc9
commit
b836b1123e
@ -191,6 +191,92 @@ namespace xgboost{
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return name_.c_str();
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return name_.c_str();
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}
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}
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};
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};
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/*! \brief Normalized DCG */
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class EvalNDCG : 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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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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}
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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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float NDCG(std::vector<std::pair<float, float>> pairs_sort) const{
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std::sort(pairs_sort.begin(), pairs_sort.end(), std::greater<float>());
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float dcg = DCG(pairs_sort);
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std::sort(pairs_sort.begin(), pairs_sort.end(), std::greater<float>());
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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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}
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};
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/*! \brief Mean Average Precision */
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class EvalMAP : 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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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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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::greater<float>());
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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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}
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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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}
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};
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};
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};
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namespace regrank{
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namespace regrank{
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@ -5,6 +5,10 @@
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* \brief implementation of objective functions
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* \brief implementation of objective functions
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* \author Tianqi Chen, Kailong Chen
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* \author Tianqi Chen, Kailong Chen
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*/
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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 xgboost{
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namespace regrank{
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namespace regrank{
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class RegressionObj : public IObjFunction{
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class RegressionObj : public IObjFunction{
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@ -202,5 +206,208 @@ namespace xgboost{
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LossType loss;
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LossType loss;
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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 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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};
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#endif
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#endif
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129
regrank/xgboost_regrank_sample.h
Normal file
129
regrank/xgboost_regrank_sample.h
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#ifndef _XGBOOST_REGRANK_SAMPLE_H_
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#define _XGBOOST_REGRANK_SAMPLE_H_
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#include <vector>
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#include"../utils/xgboost_utils.h"
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namespace xgboost {
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namespace regrank {
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namespace sample {
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/*
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* \brief the data structure to maintain the sample pairs
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* similar to the adjacency list of a graph
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*/
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struct Pairs {
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/*
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* \brief constructor given the start and end offset of the sampling group
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* in overall instances
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* \param start the begin index of the group
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* \param end the end index of the group
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*/
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Pairs(int start, int end) :start_(start), end_(end){
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for (int i = start; i < end; i++){
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std::vector<int> v;
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pairs_.push_back(v);
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}
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}
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/*
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* \brief retrieve the related pair information of an data instances
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* \param index, the index of retrieved instance
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* \return the index of instances paired
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*/
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std::vector<int> GetPairs(int index) const{
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utils::Assert(index >= start_ && index < end_, "The query index out of sampling bound");
|
||||||
|
return pairs_[index - start_];
|
||||||
|
}
|
||||||
|
|
||||||
|
/*
|
||||||
|
* \brief add in a sampled pair
|
||||||
|
* \param index the index of the instance to sample a friend
|
||||||
|
* \param paired_index the index of the instance sampled as a friend
|
||||||
|
*/
|
||||||
|
void push(int index, int paired_index){
|
||||||
|
pairs_[index - start_].push_back(paired_index);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector< std::vector<int> > pairs_;
|
||||||
|
int start_;
|
||||||
|
int end_;
|
||||||
|
};
|
||||||
|
|
||||||
|
/*
|
||||||
|
* \brief the interface of pair sampler
|
||||||
|
*/
|
||||||
|
struct IPairSampler {
|
||||||
|
/*
|
||||||
|
* \brief Generate sample pairs given the predcions, labels, the start and the end index
|
||||||
|
* of a specified group
|
||||||
|
* \param preds, the predictions of all data instances
|
||||||
|
* \param labels, the labels of all data instances
|
||||||
|
* \param start, the start index of a specified group
|
||||||
|
* \param end, the end index of a specified group
|
||||||
|
* \return the generated pairs
|
||||||
|
*/
|
||||||
|
virtual Pairs GenPairs(const std::vector<float> &preds,
|
||||||
|
const std::vector<float> &labels,
|
||||||
|
int start, int end) = 0;
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
enum{
|
||||||
|
BINARY_LINEAR_SAMPLER
|
||||||
|
};
|
||||||
|
|
||||||
|
/*! \brief A simple pair sampler when the rank relevence scale is binary
|
||||||
|
* for each positive instance, we will pick a negative
|
||||||
|
* instance and add in a pair. When using binary linear sampler,
|
||||||
|
* we should guarantee the labels are 0 or 1
|
||||||
|
*/
|
||||||
|
struct BinaryLinearSampler :public IPairSampler{
|
||||||
|
virtual Pairs GenPairs(const std::vector<float> &preds,
|
||||||
|
const std::vector<float> &labels,
|
||||||
|
int start, int end) {
|
||||||
|
Pairs pairs(start, end);
|
||||||
|
int pointer = 0, last_pointer = 0, index = start, interval = end - start;
|
||||||
|
for (int i = start; i < end; i++){
|
||||||
|
if (labels[i] == 1){
|
||||||
|
while (true){
|
||||||
|
index = (++pointer) % interval + start;
|
||||||
|
if (labels[index] == 0) break;
|
||||||
|
if (pointer - last_pointer > interval) return pairs;
|
||||||
|
}
|
||||||
|
pairs.push(i, index);
|
||||||
|
pairs.push(index, i);
|
||||||
|
last_pointer = pointer;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return pairs;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
/*! \brief Pair Sampler Wrapper*/
|
||||||
|
struct PairSamplerWrapper{
|
||||||
|
public:
|
||||||
|
inline void AssignSampler(int sampler_index){
|
||||||
|
|
||||||
|
switch (sampler_index){
|
||||||
|
case BINARY_LINEAR_SAMPLER:sampler_ = &binary_linear_sampler; break;
|
||||||
|
default:utils::Error("Cannot find the specified sampler");
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
~PairSamplerWrapper(){ delete sampler_; }
|
||||||
|
|
||||||
|
Pairs GenPairs(const std::vector<float> &preds,
|
||||||
|
const std::vector<float> &labels,
|
||||||
|
int start, int end){
|
||||||
|
utils::Assert(sampler_ != NULL, "Not config the sampler yet. Add rank:sampler in the config file\n");
|
||||||
|
return sampler_->GenPairs(preds, labels, start, end);
|
||||||
|
}
|
||||||
|
private:
|
||||||
|
BinaryLinearSampler binary_linear_sampler;
|
||||||
|
IPairSampler *sampler_;
|
||||||
|
};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
#endif
|
||||||
Loading…
x
Reference in New Issue
Block a user