mosesdecoder/mert/Scorer.cpp

#include "Scorer.h"

//regularisation strategies
static float score_min(const statscores_t& scores, size_t start, size_t end)
{
  float min = numeric_limits<float>::max();
  for (size_t i = start; i < end; ++i) {
    if (scores[i] < min) {
      min = scores[i];
    }
  }
  return min;
}

static float score_average(const statscores_t& scores, size_t start, size_t end)
{
  if ((end - start) < 1) {
    //shouldn't happen
    return 0;
  }
  float total = 0;
  for (size_t j = start; j < end; ++j) {
    total += scores[j];
  }

  return total / (end - start);
}

void  StatisticsBasedScorer::score(const candidates_t& candidates, const diffs_t& diffs,
                                   statscores_t& scores)
{
  if (!_scoreData) {
    throw runtime_error("Score data not loaded");
  }
  //calculate the score for the candidates
  if (_scoreData->size() == 0) {
    throw runtime_error("Score data is empty");
  }
  if (candidates.size() == 0) {
    throw runtime_error("No candidates supplied");
  }
  int numCounts = _scoreData->get(0,candidates[0]).size();
  vector<int> totals(numCounts);
  for (size_t i = 0; i < candidates.size(); ++i) {
    ScoreStats stats = _scoreData->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());
    }
    for (size_t k = 0; k < totals.size(); ++k) {
      totals[k] += stats.get(k);
    }
  }
  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;
      size_t last_nid = last_candidates[sid];
      for (size_t k  = 0; k < totals.size(); ++k) {
        int diff = _scoreData->get(sid,nid).get(k)
                   - _scoreData->get(sid,last_nid).get(k);
        totals[k] += diff;
      }
      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);
    }
  }
}