pro.cpp
// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2011- University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
/**
* This is part of the PRO implementation. It converts the features and scores
* files into a form suitable for input into the megam maxent trainer.
*
* For details of PRO, refer to Hopkins & May (EMNLP 2011)
**/
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <ctime>
#include <iostream>
#include <string>
#include <vector>
#include <utility>
#include <boost/program_options.hpp>
#include "BleuScorer.h"
#include "FeatureDataIterator.h"
#include "ScoreDataIterator.h"
#include "BleuScorer.h"
#include "Util.h"
#include "util/random.hh"
using namespace std;
using namespace MosesTuning;
namespace po = boost::program_options;
namespace MosesTuning
{
class SampledPair
{
private:
pair<size_t,size_t> m_translation1;
pair<size_t,size_t> m_translation2;
float m_score_diff;
public:
SampledPair(const pair<size_t,size_t>& t1, const pair<size_t,size_t>& t2, float diff ) {
if (diff > 0) {
m_translation1 = t1;
m_translation2 = t2;
m_score_diff = diff;
} else {
m_translation1 = t2;
m_translation2 = t1;
m_score_diff = -diff;
}
}
float getDiff() const {
return m_score_diff;
}
const pair<size_t,size_t>& getTranslation1() const {
return m_translation1;
}
const pair<size_t,size_t>& getTranslation2() const {
return m_translation2;
}
};
static void outputSample(ostream& out, const FeatureDataItem& f1, const FeatureDataItem& f2)
{
// difference in score in regular features
for(unsigned int j=0; j<f1.dense.size(); j++)
if (abs(f1.dense[j]-f2.dense[j]) > 0.00001)
out << " F" << j << " " << (f1.dense[j]-f2.dense[j]);
if (f1.sparse.size() || f2.sparse.size()) {
out << " ";
// sparse features
const SparseVector &s1 = f1.sparse;
const SparseVector &s2 = f2.sparse;
SparseVector diff = s1 - s2;
diff.write(out);
}
}
}
int main(int argc, char** argv)
{
bool help;
vector<string> scoreFiles;
vector<string> featureFiles;
int seed;
string outputFile;
// TODO: Add these constants to options
const unsigned int n_candidates = 5000; // Gamma, in Hopkins & May
const unsigned int n_samples = 50; // Xi, in Hopkins & May
const float min_diff = 0.05;
bool smoothBP = false;
const float bleuSmoothing = 1.0f;
po::options_description desc("Allowed options");
desc.add_options()
("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
("random-seed,r", po::value<int>(&seed), "Seed for random number generation")
("output-file,o", po::value<string>(&outputFile), "Output file")
("smooth-brevity-penalty,b", po::value(&smoothBP)->zero_tokens()->default_value(false), "Smooth the brevity penalty, as in Nakov et al. (Coling 2012)")
;
po::options_description cmdline_options;
cmdline_options.add(desc);
po::variables_map vm;
po::store(po::command_line_parser(argc,argv).
options(cmdline_options).run(), vm);
po::notify(vm);
if (help) {
cout << "Usage: " + string(argv[0]) + " [options]" << endl;
cout << desc << endl;
exit(0);
}
if (vm.count("random-seed")) {
cerr << "Initialising random seed to " << seed << endl;
util::rand_init(seed);
} else {
cerr << "Initialising random seed from system clock" << endl;
util::rand_init();
}
if (scoreFiles.size() == 0 || featureFiles.size() == 0) {
cerr << "No data to process" << endl;
exit(0);
}
if (featureFiles.size() != scoreFiles.size()) {
cerr << "Error: Number of feature files (" << featureFiles.size() <<
") does not match number of score files (" << scoreFiles.size() << ")" << endl;
exit(1);
}
ostream* out;
ofstream outFile;
if (!outputFile.empty() ) {
outFile.open(outputFile.c_str());
if (!(outFile)) {
cerr << "Error: Failed to open " << outputFile << endl;
exit(1);
}
out = &outFile;
} else {
out = &cout;
}
vector<FeatureDataIterator> featureDataIters;
vector<ScoreDataIterator> scoreDataIters;
for (size_t i = 0; i < featureFiles.size(); ++i) {
featureDataIters.push_back(FeatureDataIterator(featureFiles[i]));
scoreDataIters.push_back(ScoreDataIterator(scoreFiles[i]));
}
//loop through nbest lists
size_t sentenceId = 0;
while(1) {
vector<pair<size_t,size_t> > hypotheses;
//TODO: de-deuping. Collect hashes of score,feature pairs and
//only add index if it's unique.
if (featureDataIters[0] == FeatureDataIterator::end()) {
break;
}
for (size_t i = 0; i < featureFiles.size(); ++i) {
if (featureDataIters[i] == FeatureDataIterator::end()) {
cerr << "Error: Feature file " << i << " ended prematurely" << endl;
exit(1);
}
if (scoreDataIters[i] == ScoreDataIterator::end()) {
cerr << "Error: Score file " << i << " ended prematurely" << endl;
exit(1);
}
if (featureDataIters[i]->size() != scoreDataIters[i]->size()) {
cerr << "Error: For sentence " << sentenceId << " features and scores have different size" << endl;
exit(1);
}
for (size_t j = 0; j < featureDataIters[i]->size(); ++j) {
hypotheses.push_back(pair<size_t,size_t>(i,j));
}
}
//collect the candidates
vector<SampledPair> samples;
vector<float> scores;
size_t n_translations = hypotheses.size();
for(size_t i=0; i<n_candidates; i++) {
size_t rand1 = util::rand_excl(n_translations);
pair<size_t,size_t> translation1 = hypotheses[rand1];
float bleu1 = smoothedSentenceBleu(scoreDataIters[translation1.first]->operator[](translation1.second), bleuSmoothing, smoothBP);
size_t rand2 = util::rand_excl(n_translations);
pair<size_t,size_t> translation2 = hypotheses[rand2];
float bleu2 = smoothedSentenceBleu(scoreDataIters[translation2.first]->operator[](translation2.second), bleuSmoothing, smoothBP);
/*
cerr << "t(" << translation1.first << "," << translation1.second << ") = " << bleu1 <<
" t(" << translation2.first << "," << translation2.second << ") = " <<
bleu2 << " diff = " << abs(bleu1-bleu2) << endl;
*/
if (abs(bleu1-bleu2) < min_diff)
continue;
samples.push_back(SampledPair(translation1, translation2, bleu1-bleu2));
scores.push_back(1.0-abs(bleu1-bleu2));
}
float sample_threshold = -1.0;
if (samples.size() > n_samples) {
NTH_ELEMENT3(scores.begin(), scores.begin() + (n_samples-1), scores.end());
sample_threshold = 0.99999-scores[n_samples-1];
}
size_t collected = 0;
for (size_t i = 0; collected < n_samples && i < samples.size(); ++i) {
if (samples[i].getDiff() < sample_threshold) continue;
++collected;
size_t file_id1 = samples[i].getTranslation1().first;
size_t hypo_id1 = samples[i].getTranslation1().second;
size_t file_id2 = samples[i].getTranslation2().first;
size_t hypo_id2 = samples[i].getTranslation2().second;
*out << "1";
outputSample(*out, featureDataIters[file_id1]->operator[](hypo_id1),
featureDataIters[file_id2]->operator[](hypo_id2));
*out << endl;
*out << "0";
outputSample(*out, featureDataIters[file_id2]->operator[](hypo_id2),
featureDataIters[file_id1]->operator[](hypo_id1));
*out << endl;
}
//advance all iterators
for (size_t i = 0; i < featureFiles.size(); ++i) {
++featureDataIters[i];
++scoreDataIters[i];
}
++sentenceId;
}
outFile.close();
}
