#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"

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){}
        };

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

    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() );
            }
            ~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