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/*
EGYPT Toolkit for Statistical Machine Translation
Written by Yaser Al-Onaizan, Jan Curin, Michael Jahr, Kevin Knight, John Lafferty, Dan Melamed, David Purdy, Franz Och, Noah Smith, and David Yarowsky.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.
This program 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307,
USA.
*/
#include <strstream>
#include <ctime>
#include <set>
#include "defs.h"
#include "vocab.h"
#include "Perplexity.h"
#include "getSentence.h"
#include "TTables.h"
#include "Globals.h"
#include "Parameter.h"
void printHelp(void)
{
cerr << "Usage:\n\n" << Usage << '\n';
cerr << "Options (these override parameters set in the config file):\n\n";
cerr << "\t--v \t\t print verbose message, Warning this is not very descriptive and not systematic.\n";
cerr << "\t--NODUMPS \t Do not write any files to disk (This will over write dump frequency options).\n";
cerr << "\t--h[elp]\t\tprint this help\n";
cerr << "\t--p\t\tUse pegging when generating alignments for Model3 training. (Default NO PEGGING)\n";
cerr << "\t--st\t\tto use a fixed ditribution for the fertility parameters when tranfering from model 2 to model 3 (Default complicated estimation)\n";
printGIZAPars(cout);
}
void generatePerplexityReport(const Perplexity& trainperp,
const Perplexity& testperp,
const Perplexity& trainVperp,
const Perplexity& testVperp,
ostream& of, int trainsize, int testsize,
bool)
{
unsigned int i, m;
unsigned int m1 = max(trainperp.size(), testperp.size());
unsigned int m2 = max(trainVperp.size(), testVperp.size());
m = max(m1,m2);
of << "#trnsz\ttstsz\titer\tmodel\ttrn-pp\t\ttest-pp\t\ttrn-vit-pp\t\ttst-vit-pp\n";
for (i = 0 ; i <m ; i++){
of << trainsize << '\t' << testsize << '\t' << i<< '\t' << trainperp.modelid[i] << '\t';
if (i < trainperp.perp.size())
of << trainperp.perp[i] << "\t\t" ;
else
of << "N/A\t\t";
if (i<testperp.perp.size())
of << testperp.perp[i] << "\t\t" ;
else
of << "N/A\t\t";
if (i < trainVperp.perp.size())
of << trainVperp.perp[i] << "\t\t" ;
else
of << "N/A\t";
if (i< testVperp.perp.size())
of << testVperp.perp[i] << '\n' ;
else
of << "N/A\n";
}
}
void printSentencePair(Vector<WordIndex>& es,
Vector<WordIndex>& fs,
ostream& of)
// just writes a sentece pair to the give output stream, one sentence pair line
// it writes token ids not actual tokens.
{
WordIndex i, j, l, m;
l = es.size() - 1;
m = fs.size() - 1;
of << "Source sentence length : " << l << " , target : " << m << "\n";
for (i = 1 ; i <= l ; i++)
of << es[i] << ' ';
of << "\n";
for (j = 1 ; j <= m ; j++)
of << fs[j] << ' ';
of << "\n";
}
extern short CompactAlignmentFormat;
void printAlignToFile(const Vector<WordIndex>& es,
const Vector<WordIndex>& fs,
const Vector<WordEntry>& evlist,
const Vector<WordEntry>& fvlist,
ostream& of2,
const Vector<WordIndex>& viterbi_alignment,
int pair_no, double alignment_score)
// prints the given alignment to alignments file (given it stream pointer)
// in a format recognizable by the draw-alignment tool ... which is of the
// example (each line triple is one sentence pair):
// # sentence caption
// target_word_1 target_word_2 ..... target_word_m
// source_word_1 ({ x y z }) source_word_2 ({ }) .. source_word_n ({w})
// where x, y, z, and w are positions of target words that each source word
// is connected to.
{
WordIndex l, m;
Vector<Vector<WordIndex> > translations(es.size()); // each english words has a vector
// of zero or more translations .
l = es.size() - 1;
m = fs.size() - 1;
if( CompactAlignmentFormat )
{
for (WordIndex j = 1 ; j <= m ; j++)
if( viterbi_alignment[j] )
of2 << viterbi_alignment[j]-1 << ' ' << j-1 << ' ';
of2 << '\n';
}
else
{
of2 << "# Sentence pair (" << pair_no <<") source length " << l << " target length "<< m <<
" alignment score : "<< alignment_score << '\n';
for (WordIndex j = 1 ; j <= m ; j++){
of2 << fvlist[fs[j]].word << " " ;
translations[viterbi_alignment[j]].push_back(j);
}
of2 << '\n';
for (WordIndex i = 0 ; i <= l ; i++){
of2 << evlist[es[i]].word << " ({ " ;
for (WordIndex j = 0 ; j < translations[i].size() ; j++)
of2 << translations[i][j] << " " ;
of2 << "}) ";
}
of2 << '\n';
}
}
void printOverlapReport(const tmodel<COUNT, PROB>& tTable,
sentenceHandler& testHandler, vcbList& trainEList,
vcbList& trainFList, vcbList& testEList, vcbList& testFList)
{
set<pair<WordIndex, WordIndex> > testCoocur ;
sentPair s ;
/* string unseenCoocurFile = Prefix + ".tst.unseen.cooc" ;
ofstream of_unseenCoocur(unseenCoocurFile.c_str());
string seenCoocurFile = Prefix + ".tst.seen.cooc" ;
ofstream of_seenCoocur(seenCoocurFile.c_str());
*/
testHandler.rewind();
int seen_coocur = 0, unseen_coocur = 0, srcUnk = 0, trgUnk = 0 ;
while(testHandler.getNextSentence(s)){
for (WordIndex i = 1 ; i < s.eSent.size() ; i++)
for (WordIndex j = 1 ; j < s.fSent.size() ; j++)
testCoocur.insert(pair<WordIndex, WordIndex> (s.eSent[i], s.fSent[j])) ;
}
set<pair<WordIndex, WordIndex> >::const_iterator i ;
for (i = testCoocur.begin() ; i != testCoocur.end() ; ++i){
if (tTable.getProb((*i).first, (*i).second) > PROB_SMOOTH){
seen_coocur ++ ;
// of_seenCoocur << (*i).first << ' ' << (*i).second << '\n';
}
else {
unseen_coocur++;
// of_unseenCoocur << (*i).first << ' ' << (*i).second << '\n';
}
}
string trgUnkFile = Prefix + ".tst.trg.unk" ;
ofstream of_trgUnk(trgUnkFile.c_str());
for (WordIndex i = 0 ; i < testFList.getVocabList().size() && i < testFList.uniqTokens();i++)
if (testFList.getVocabList()[i].freq > 0 && trainFList.getVocabList()[i].freq <= 0){
of_trgUnk << i << ' ' << testFList.getVocabList()[i].word << ' ' << testFList.getVocabList()[i].freq
<< '\n';
trgUnk++ ;
}
string srcUnkFile = Prefix + ".tst.src.unk" ;
ofstream of_srcUnk(srcUnkFile.c_str());
for (WordIndex j = 0 ; j < testEList.getVocabList().size() && j < testEList.uniqTokens();j++)
if (testEList.getVocabList()[j].freq > 0 && trainEList.getVocabList()[j].freq <= 0){
srcUnk++ ;
of_srcUnk << j << ' ' << testEList.getVocabList()[j].word << ' ' << testEList.getVocabList()[j].freq
<< '\n';
}
string summaryFile = Prefix + ".tst.stats" ;
ofstream of_summary(summaryFile.c_str());
of_summary << "\t\t STATISTICS ABOUT TEST CORPUS\n\n";
of_summary << "source unique tokens: " << testEList.uniqTokens() << '\n';
of_summary << "target unique tokens: " << testFList.uniqTokens() << '\n';
of_summary << "unique unseen source tokens: " << srcUnk << '\n';
of_summary << "unique unseen target tokens: " << trgUnk << '\n';
of_summary << "cooccurrences not found in the final t table: " << unseen_coocur << '\n';
of_summary << "cooccurrences found in the final t table: " << seen_coocur << '\n';
}
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