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