#ifndef XGBOOST_REGRANK_EVAL_H
#define XGBOOST_REGRANK_EVAL_H
/*!
* \file xgboost_regrank_eval.h
* \brief evaluation metrics for regression and classification and rank
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
*/

#include <cmath>
#include <vector>
#include <algorithm>
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_omp.h"
#include "../utils/xgboost_random.h"
#include "xgboost_regrank_data.h"
#include <functional>
#include <tuple>

namespace xgboost{
    namespace regrank{
        /*! \brief evaluator that evaluates the loss metrics */
        struct IEvaluator{
            /*!
             * \brief evaluate a specific metric
             * \param preds prediction
             * \param info information, including label etc.
             */
            virtual float Eval(const std::vector<float> &preds,
            const DMatrix::Info &info) const = 0;
            /*! \return name of metric */
            virtual const char *Name(void) const = 0;
            /*! \brief virtual destructor */
            virtual ~IEvaluator(void){}
        };

        inline static bool CmpFirst(const std::pair<float, unsigned> &a, const std::pair<float, unsigned> &b){
            return a.first > b.first;
        }

        /*! \brief RMSE */
        struct EvalRMSE : public IEvaluator{
            virtual float Eval(const std::vector<float> &preds,
            const DMatrix::Info &info) const {
                const unsigned ndata = static_cast<unsigned>(preds.size());
                float sum = 0.0, wsum = 0.0;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
                for (unsigned i = 0; i < ndata; ++i){
                    const float wt = info.GetWeight(i);
                    const float diff = info.labels[i] - preds[i];
                    sum += diff*diff * wt;
                    wsum += wt;
                }
                return sqrtf(sum / wsum);
            }
            virtual const char *Name(void) const{
                return "rmse";
            }
        };

        /*! \brief Error */
        struct EvalLogLoss : public IEvaluator{
            virtual float Eval(const std::vector<float> &preds,
            const DMatrix::Info &info) const {
                const unsigned ndata = static_cast<unsigned>(preds.size());
                float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
                for (unsigned i = 0; i < ndata; ++i){
                    const float y = info.labels[i];
                    const float py = preds[i];
                    const float wt = info.GetWeight(i);
                    sum -= wt * (y * std::log(py) + (1.0f - y)*std::log(1 - py));
                    wsum += wt;
                }
                return sum / wsum;
            }
            virtual const char *Name(void) const{
                return "negllik";
            }
        };

        /*! \brief Error */
        struct EvalError : public IEvaluator{
            virtual float Eval(const std::vector<float> &preds,
            const DMatrix::Info &info) const {
                const unsigned ndata = static_cast<unsigned>(preds.size());
                float sum = 0.0f, wsum = 0.0f;
#pragma omp parallel for reduction(+:sum,wsum) schedule( static )
                for (unsigned i = 0; i < ndata; ++i){
                    const float wt = info.GetWeight(i);
                    if (preds[i] > 0.5f){
                        if (info.labels[i] < 0.5f) sum += wt;
                    }
                    else{
                        if (info.labels[i] >= 0.5f) sum += wt;
                    }
                    wsum += wt;
                }
                return sum / wsum;
            }
            virtual const char *Name(void) const{
                return "error";
            }
        };

        /*! \brief Area under curve, for both classification and rank */
        struct EvalAuc : public IEvaluator{
            virtual float Eval(const std::vector<float> &preds,
            const DMatrix::Info &info) const {
                std::vector<unsigned> tgptr(2, 0); tgptr[1] = preds.size();
                const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
                utils::Assert(gptr.back() == preds.size(), "EvalAuc: group structure must match number of prediction");
                const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);

                double sum_auc = 0.0f;
#pragma omp parallel reduction(+:sum_auc) 
                {
                    // each thread takes a local rec
                    std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static) 
                    for (unsigned k = 0; k < ngroup; ++k){
                        rec.clear();
                        for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j){
                            rec.push_back(std::make_pair(preds[j], j));
                        }
                        std::sort(rec.begin(), rec.end(), CmpFirst);
                        // calculate AUC
                        double sum_pospair = 0.0;
                        double sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
                        for (size_t j = 0; j < rec.size(); ++j){
                            const float wt = info.GetWeight(rec[j].second);
                            const float ctr = info.labels[rec[j].second];
                            // keep bucketing predictions in same bucket
                            if (j != 0 && rec[j].first != rec[j - 1].first){
                                sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
                                sum_npos += buf_pos; sum_nneg += buf_neg;
                                buf_neg = buf_pos = 0.0f;
                            }
                            buf_pos += ctr * wt; buf_neg += (1.0f - ctr) * wt;
                        }
                        sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
                        sum_npos += buf_pos; sum_nneg += buf_neg;
                        // 
                        utils::Assert(sum_npos > 0.0 && sum_nneg > 0.0, "the dataset only contains pos or neg samples");
                        // this is the AUC
                        sum_auc += sum_pospair / (sum_npos*sum_nneg);
                    }
                }
                // return average AUC over list
                return static_cast<float>(sum_auc) / ngroup;
            }
            virtual const char *Name(void) const{
                return "auc";
            }
        };

        /*! \brief Precison at N, for both classification and rank */
        struct EvalPrecision : public IEvaluator{
            unsigned topn_;
            std::string name_;
            EvalPrecision(const char *name){
                name_ = name;
                utils::Assert(sscanf(name, "pre@%u", &topn_));
            }
            virtual float Eval(const std::vector<float> &preds,
                const DMatrix::Info &info) const {
                const std::vector<unsigned> &gptr = info.group_ptr;
                utils::Assert(gptr.size() != 0 && gptr.back() == preds.size(), "EvalAuc: group structure must match number of prediction");
                const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);

                double sum_pre = 0.0f;
#pragma omp parallel reduction(+:sum_pre) 
                {
                    // each thread takes a local rec
                    std::vector< std::pair<float, unsigned> > rec;
#pragma omp for schedule(static) 
                    for (unsigned k = 0; k < ngroup; ++k){
                        rec.clear();
                        for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j){
                            rec.push_back(std::make_pair(preds[j], (int)info.labels[j]));
                        }
                        std::sort(rec.begin(), rec.end(), CmpFirst);
                        // calculate Preicsion
                        unsigned nhit = 0;
                        for (size_t j = 0; j < rec.size() && j < topn_; ++j){
                            nhit += rec[j].second;
                        }
                        sum_pre += ((float)nhit) / topn_;
                    }
                }
                return static_cast<float>(sum_pre) / ngroup;
            }
            virtual const char *Name(void) const{
                return name_.c_str();
            }
        };

        /*! \brief Normalized DCG */
        class EvalNDCG : public IEvaluator {
        public:
            virtual float Eval(const std::vector<float> &preds,
                const DMatrix::Info &info) const{
                if (info.group_ptr.size() <= 1) return 0;
                float acc = 0;
                std::vector< std::pair<float, float> > pairs_sort;
                for (int i = 0; i < info.group_ptr.size() - 1; i++){
                    for (int j = info.group_ptr[i]; j < info.group_ptr[i + 1]; j++){
                        pairs_sort.push_back(std::make_pair(preds[j], info.labels[j]));
                    }
                    acc += NDCG(pairs_sort);
                }
                return acc / (info.group_ptr.size() - 1);
            }

            static float DCG(const std::vector<float> &labels){
                float ans = 0.0;
                for (int i = 0; i < labels.size(); i++){
                    ans += (pow(2, labels[i]) - 1) / log(i + 2);
                }
                return ans;
            }

            virtual const char *Name(void) const {
                return "NDCG";
            }

        private:
            /*\brief Obtain NDCG given the list of labels and predictions
            * \param pairs_sort the first field is prediction and the second is label
            */
            float NDCG(std::vector< std::pair<float, float> > pairs_sort) const{
                std::sort(pairs_sort.begin(), pairs_sort.end(), [](std::pair<float, float> a, std::pair<float, float> b){
                    return std::get<0>(a) > std::get<0>(b);
                });
                float dcg = DCG(pairs_sort);
                std::sort(pairs_sort.begin(), pairs_sort.end(), [](std::pair<float, float> a, std::pair<float, float> b){
                    return std::get<1>(a) > std::get<1>(b);
                });
                float IDCG = DCG(pairs_sort);
                if (IDCG == 0) return 0;
                return dcg / IDCG;
            }

            float DCG(std::vector< std::pair<float, float> > pairs_sort) const{
                std::vector<float> labels;
                for (int i = 1; i < pairs_sort.size(); i++){
                    labels.push_back(std::get<1>(pairs_sort[i]));
                }
                return DCG(labels);
            }
        };


        /*! \brief Mean Average Precision */
        class EvalMAP : public IEvaluator {
        public:
            virtual float Eval(const std::vector<float> &preds,
                const DMatrix::Info &info) const{
                if (info.group_ptr.size() <= 1) return 0;
                float acc = 0;
                std::vector<std::pair<float,float>> pairs_sort;
                for (int i = 0; i < info.group_ptr.size() - 1; i++){
                    for (int j = info.group_ptr[i]; j < info.group_ptr[i + 1]; j++){
                        pairs_sort.push_back(std::make_pair(preds[j], info.labels[j]));
                    }
                    acc += average_precision(pairs_sort);
                }
                return acc / (info.group_ptr.size() - 1);
            }

            virtual const char *Name(void) const {
                return "MAP";
            }

        private:
            /*\brief Obtain average precision given the list of labels and predictions
            * \param pairs_sort the first field is prediction and the second is label
            */
            float average_precision(std::vector< std::pair<float,float> > pairs_sort) const{
                std::sort(pairs_sort.begin(), pairs_sort.end(), [](std::pair<float, float> a, std::pair<float, float> b){
                    return std::get<0>(a) > std::get<0>(b);
                });
                float hits = 0;
                float average_precision = 0;
                for (int j = 0; j < pairs_sort.size(); j++){
                    if (std::get<1>(pairs_sort[j]) == 1){
                        hits++;
                        average_precision += hits / (j + 1);
                    }
                }
                if (hits != 0) average_precision /= hits;
                return average_precision;
            }
        };
    };

    namespace regrank{
        /*! \brief a set of evaluators */
        struct EvalSet{
        public:
            inline void AddEval(const char *name){
                for (size_t i = 0; i < evals_.size(); ++i){
                    if (!strcmp(name, evals_[i]->Name())) return;
                }
                if (!strcmp(name, "rmse"))    evals_.push_back(new EvalRMSE());
                if (!strcmp(name, "error"))   evals_.push_back(new EvalError());
                if (!strcmp(name, "logloss")) evals_.push_back(new EvalLogLoss());
                if (!strcmp(name, "auc"))    evals_.push_back(new EvalAuc());
                if (!strncmp(name, "pre@", 4)) evals_.push_back(new EvalPrecision(name));
            }
            ~EvalSet(){
                for (size_t i = 0; i < evals_.size(); ++i){
                    delete evals_[i];
                }
            }
            inline void Eval(FILE *fo, const char *evname,
                const std::vector<float> &preds,
                const DMatrix::Info &info) const{
                for (size_t i = 0; i < evals_.size(); ++i){
                    float res = evals_[i]->Eval(preds, info);
                    fprintf(fo, "\t%s-%s:%f", evname, evals_[i]->Name(), res);
                }
            }
        private:
            std::vector<const IEvaluator*> evals_;
        };
    };
};
#endif