mosesdecoder/mert/SentenceLevelScorer.cpp

//
//  SentenceLevelScorer.cpp
//  mert_lib
//
//  Created by Hieu Hoang on 22/06/2012.
//  Copyright 2012 __MyCompanyName__. All rights reserved.
//

#include <iostream>
#include "SentenceLevelScorer.h"

using namespace std;


/** The sentence level scores have already been calculated, just need to average them
 and include the differences. Allows scores which are floats **/
void  SentenceLevelScorer::score(const candidates_t& candidates, const diffs_t& diffs,
                                 statscores_t& scores)
{
  //cout << "*******SentenceLevelScorer::score" << endl;
  if (!m_score_data) {
    throw runtime_error("Score data not loaded");
  }
  //calculate the score for the candidates
  if (m_score_data->size() == 0) {
    throw runtime_error("Score data is empty");
  }
  if (candidates.size() == 0) {
    throw runtime_error("No candidates supplied");
  }
  int numCounts = m_score_data->get(0,candidates[0]).size();
  vector<float> totals(numCounts);
  for (size_t i = 0; i < candidates.size(); ++i) {
    //cout << " i " << i << " candi " << candidates[i] ;
    ScoreStats stats = m_score_data->get(i,candidates[i]);
    if (stats.size() != totals.size()) {
      stringstream msg;
      msg << "Statistics for (" << "," << candidates[i] << ") have incorrect "
      << "number of fields. Found: " << stats.size() << " Expected: "
      << totals.size();
      throw runtime_error(msg.str());
    }
    //Add up scores for all sentences, would normally be just one score
    for (size_t k = 0; k < totals.size(); ++k) {
      totals[k] += stats.get(k);
      //cout << " stats " << stats.get(k) ;
    }
    //cout << endl;
  }
  //take average
  for (size_t k = 0; k < totals.size(); ++k) {
    //cout << "totals = " << totals[k] << endl;
    //cout << "cand = " << candidates.size() << endl;
    totals[k] /= candidates.size();
    //cout << "finaltotals = " << totals[k] << endl;
  }
  
  scores.push_back(calculateScore(totals));
  
  candidates_t last_candidates(candidates);
  //apply each of the diffs, and get new scores
  for (size_t i = 0; i < diffs.size(); ++i) {
    for (size_t j = 0; j < diffs[i].size(); ++j) {
      size_t sid = diffs[i][j].first;
      size_t nid = diffs[i][j].second;
      //cout << "sid = " << sid << endl;
      //cout << "nid = " << nid << endl;
      size_t last_nid = last_candidates[sid];
      for (size_t k  = 0; k < totals.size(); ++k) {
        float diff = m_score_data->get(sid,nid).get(k)
        - m_score_data->get(sid,last_nid).get(k);
        //cout << "diff = " << diff << endl;
        totals[k] += diff/candidates.size();
        //cout << "totals = " << totals[k] << endl;
      }
      last_candidates[sid] = nid;
    }
    scores.push_back(calculateScore(totals));
  }
  
  //regularisation. This can either be none, or the min or average as described in
  //Cer, Jurafsky and Manning at WMT08
  if (_regularisationStrategy == REG_NONE || _regularisationWindow <= 0) {
    //no regularisation
    return;
  }
  
  //window size specifies the +/- in each direction
  statscores_t raw_scores(scores);//copy scores
  for (size_t i = 0; i < scores.size(); ++i) {
    size_t start = 0;
    if (i >= _regularisationWindow) {
      start = i - _regularisationWindow;
    }
    size_t end = min(scores.size(), i + _regularisationWindow+1);
    if (_regularisationStrategy == REG_AVERAGE) {
      scores[i] = score_average(raw_scores,start,end);
    } else {
      scores[i] = score_min(raw_scores,start,end);
    }
  }
}