316 lines
14 KiB
C++
316 lines
14 KiB
C++
#ifndef _XGBOOST_REGRANK_SAMPLE_H_
|
|
#define _XGBOOST_REGRANK_SAMPLE_H_
|
|
#include <vector>
|
|
#include"../utils/xgboost_utils.h"
|
|
|
|
namespace xgboost {
|
|
namespace regrank {
|
|
namespace sample {
|
|
|
|
/*
|
|
* \brief the data structure to maintain the sample pairs
|
|
* similar to the adjacency list of a graph
|
|
*/
|
|
struct Pairs {
|
|
|
|
/*
|
|
* \brief constructor given the start and end offset of the sampling group
|
|
* in overall instances
|
|
* \param start the begin index of the group
|
|
* \param end the end index of the group
|
|
*/
|
|
Pairs(int start, int end) :start_(start), end_(end){
|
|
for (int i = start; i < end; i++){
|
|
std::vector<int> v;
|
|
pairs_.push_back(v);
|
|
}
|
|
}
|
|
/*
|
|
* \brief retrieve the related pair information of an data instances
|
|
* \param index, the index of retrieved instance
|
|
* \return the index of instances paired
|
|
*/
|
|
std::vector<int> GetPairs(int index) const{
|
|
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_;
|
|
};
|
|
}
|
|
}
|
|
|
|
|
|
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
|