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128 lines
4.0 KiB
C++
128 lines
4.0 KiB
C++
//
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// StatisticsBasedScorer.cpp
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// mert_lib
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//
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// Created by Hieu Hoang on 23/06/2012.
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// Copyright 2012 __MyCompanyName__. All rights reserved.
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//
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#include <iostream>
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#include "StatisticsBasedScorer.h"
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using namespace std;
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namespace MosesTuning
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{
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StatisticsBasedScorer::StatisticsBasedScorer(const string& name, const string& config)
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: Scorer(name,config)
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{
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//configure regularisation
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static string KEY_TYPE = "regtype";
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static string KEY_WINDOW = "regwin";
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static string KEY_CASE = "case";
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static string TYPE_NONE = "none";
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static string TYPE_AVERAGE = "average";
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static string TYPE_MINIMUM = "min";
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static string TRUE = "true";
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static string FALSE = "false";
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string type = getConfig(KEY_TYPE,TYPE_NONE);
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if (type == TYPE_NONE) {
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m_regularization_type = NONE;
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} else if (type == TYPE_AVERAGE) {
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m_regularization_type = AVERAGE;
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} else if (type == TYPE_MINIMUM) {
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m_regularization_type = MINIMUM;
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} else {
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throw runtime_error("Unknown scorer regularisation strategy: " + type);
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}
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// cerr << "Using scorer regularisation strategy: " << type << endl;
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const string& window = getConfig(KEY_WINDOW, "0");
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m_regularization_window = atoi(window.c_str());
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// cerr << "Using scorer regularisation window: " << m_regularization_window << endl;
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const string& preserve_case = getConfig(KEY_CASE,TRUE);
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if (preserve_case == TRUE) {
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m_enable_preserve_case = true;
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} else if (preserve_case == FALSE) {
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m_enable_preserve_case = false;
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}
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// cerr << "Using case preservation: " << m_enable_preserve_case << endl;
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}
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void StatisticsBasedScorer::score(const candidates_t& candidates, const diffs_t& diffs,
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statscores_t& scores) const
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{
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if (!m_score_data) {
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throw runtime_error("Score data not loaded");
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}
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// calculate the score for the candidates
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if (m_score_data->size() == 0) {
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throw runtime_error("Score data is empty");
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}
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if (candidates.size() == 0) {
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throw runtime_error("No candidates supplied");
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}
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int numCounts = m_score_data->get(0,candidates[0]).size();
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vector<ScoreStatsType> totals(numCounts);
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for (size_t i = 0; i < candidates.size(); ++i) {
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ScoreStats stats = m_score_data->get(i,candidates[i]);
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if (stats.size() != totals.size()) {
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stringstream msg;
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msg << "Statistics for (" << "," << candidates[i] << ") have incorrect "
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<< "number of fields. Found: " << stats.size() << " Expected: "
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<< totals.size();
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throw runtime_error(msg.str());
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}
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for (size_t k = 0; k < totals.size(); ++k) {
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totals[k] += stats.get(k);
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}
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}
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scores.push_back(calculateScore(totals));
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candidates_t last_candidates(candidates);
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// apply each of the diffs, and get new scores
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for (size_t i = 0; i < diffs.size(); ++i) {
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for (size_t j = 0; j < diffs[i].size(); ++j) {
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size_t sid = diffs[i][j].first;
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size_t nid = diffs[i][j].second;
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size_t last_nid = last_candidates[sid];
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for (size_t k = 0; k < totals.size(); ++k) {
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int diff = m_score_data->get(sid,nid).get(k)
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- m_score_data->get(sid,last_nid).get(k);
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totals[k] += diff;
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}
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last_candidates[sid] = nid;
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}
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scores.push_back(calculateScore(totals));
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}
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// Regularisation. This can either be none, or the min or average as described in
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// Cer, Jurafsky and Manning at WMT08.
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if (m_regularization_type == NONE || m_regularization_window <= 0) {
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// no regularisation
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return;
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}
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// window size specifies the +/- in each direction
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statscores_t raw_scores(scores); // copy scores
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for (size_t i = 0; i < scores.size(); ++i) {
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size_t start = 0;
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if (i >= m_regularization_window) {
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start = i - m_regularization_window;
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}
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const size_t end = min(scores.size(), i + m_regularization_window + 1);
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if (m_regularization_type == AVERAGE) {
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scores[i] = score_average(raw_scores,start,end);
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} else {
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scores[i] = score_min(raw_scores,start,end);
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}
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}
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}
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}
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