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//
// 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);
}
}
}
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