rank pass toy
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kalenhaha
@@ -18,133 +18,133 @@
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#include "../base/xgboost_learner.h"
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namespace xgboost {
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namespace rank {
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/*! \brief class for gradient boosted regression */
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class RankBoostLearner :public base::BoostLearner{
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public:
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/*! \brief constructor */
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RankBoostLearner(void) {
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BoostLearner();
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}
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/*!
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* \brief a rank booster associated with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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RankBoostLearner(const base::DMatrix *train,
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const std::vector<base::DMatrix *> &evals,
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const std::vector<std::string> &evname) {
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namespace rank {
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/*! \brief class for gradient boosted regression */
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class RankBoostLearner :public base::BoostLearner{
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public:
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/*! \brief constructor */
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RankBoostLearner(void) {
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BoostLearner();
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}
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/*!
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* \brief a rank booster associated with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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RankBoostLearner(const base::DMatrix *train,
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const std::vector<base::DMatrix *> &evals,
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const std::vector<std::string> &evname) {
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BoostLearner(train, evals, evname);
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}
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BoostLearner(train, evals, evname);
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}
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/*!
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* \brief initialize solver before training, called before training
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* this function is reserved for solver to allocate necessary space
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* and do other preparation
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*/
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inline void InitTrainer(void) {
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BoostLearner::InitTrainer();
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if (mparam.loss_type == PAIRWISE) {
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evaluator_.AddEval("PAIR");
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}
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else if (mparam.loss_type == MAP) {
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evaluator_.AddEval("MAP");
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}
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else {
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evaluator_.AddEval("NDCG");
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}
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evaluator_.Init();
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}
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/*!
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* \brief initialize solver before training, called before training
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* this function is reserved for solver to allocate necessary space
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* and do other preparation
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*/
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inline void InitTrainer(void) {
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BoostLearner::InitTrainer();
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if (mparam.loss_type == PAIRWISE) {
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evaluator_.AddEval("PAIR");
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}
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else if (mparam.loss_type == MAP) {
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evaluator_.AddEval("MAP");
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}
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else {
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evaluator_.AddEval("NDCG");
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}
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evaluator_.Init();
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}
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void EvalOneIter(int iter, FILE *fo = stderr) {
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fprintf(fo, "[%d]", iter);
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int buffer_offset = static_cast<int>(train_->Size());
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void EvalOneIter(int iter, FILE *fo = stderr) {
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fprintf(fo, "[%d]", iter);
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int buffer_offset = static_cast<int>(train_->Size());
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for (size_t i = 0; i < evals_.size(); ++i) {
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std::vector<float> &preds = this->eval_preds_[i];
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this->PredictBuffer(preds, *evals_[i], buffer_offset);
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evaluator_.Eval(fo, evname_[i].c_str(), preds, (*evals_[i]).labels, (*evals_[i]).group_index);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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fprintf(fo, "\n");
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}
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for (size_t i = 0; i < evals_.size(); ++i) {
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std::vector<float> &preds = this->eval_preds_[i];
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this->PredictBuffer(preds, *evals_[i], buffer_offset);
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evaluator_.Eval(fo, evname_[i].c_str(), preds, (*evals_[i]).labels, (*evals_[i]).group_index);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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fprintf(fo, "\n");
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}
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inline void SetParam(const char *name, const char *val){
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if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
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if (!strcmp(name, "rank:sampler")) sampler.AssignSampler(atoi(val));
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}
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels */
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inline void GetGradient(const std::vector<float> &preds,
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const std::vector<float> &labels,
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const std::vector<int> &group_index,
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std::vector<float> &grad,
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std::vector<float> &hess) {
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grad.resize(preds.size());
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hess.resize(preds.size());
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bool j_better;
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float pred_diff, pred_diff_exp, first_order_gradient, second_order_gradient;
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for (int i = 0; i < group_index.size() - 1; i++){
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sample::Pairs pairs = sampler.GenPairs(preds, labels, group_index[i], group_index[i + 1]);
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for (int j = group_index[i]; j < group_index[i + 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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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 = FirstOrderGradient(pred_diff_exp);
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second_order_gradient = 2 * 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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inline void SetParam(const char *name, const char *val){
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if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
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if (!strcmp(name, "rank:sampler")) sampler.AssignSampler(atoi(val));
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}
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels */
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inline void GetGradient(const std::vector<float> &preds,
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const std::vector<float> &labels,
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const std::vector<int> &group_index,
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std::vector<float> &grad,
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std::vector<float> &hess) {
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grad.resize(preds.size());
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hess.resize(preds.size());
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bool j_better;
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float pred_diff, pred_diff_exp, first_order_gradient, second_order_gradient;
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for (int i = 0; i < group_index.size() - 1; i++){
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sample::Pairs pairs = sampler.GenPairs(preds, labels, group_index[i], group_index[i + 1]);
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for (int j = group_index[i]; j < group_index[i + 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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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 = FirstOrderGradient(pred_diff_exp);
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second_order_gradient = 2 * 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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inline void UpdateInteract(std::string action) {
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}
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private:
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enum LossType {
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PAIRWISE = 0,
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MAP = 1,
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NDCG = 2
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};
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inline void UpdateInteract(std::string action) {
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}
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private:
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enum LossType {
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PAIRWISE = 0,
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MAP = 1,
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NDCG = 2
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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 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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/*!
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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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RankEvalSet evaluator_;
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sample::PairSamplerWrapper sampler;
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};
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};
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private:
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RankEvalSet evaluator_;
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sample::PairSamplerWrapper sampler;
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};
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};
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};
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#endif
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