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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 "model2.h"
#include "Globals.h"
#include "utility.h"
#include "Parameter.h"
#include "defs.h"
extern short NoEmptyWord;
GLOBAL_PARAMETER2(int,Model2_Dump_Freq,"MODEL 2 DUMP FREQUENCY","t2","dump frequency of Model 2",PARLEV_OUTPUT,0);
model2::model2(model1& m,amodel<PROB>&_aTable,amodel<COUNT>&_aCountTable):
model1(m),aTable(_aTable),aCountTable(_aCountTable)
{ }
void model2::initialize_table_uniformly(sentenceHandler& sHandler1)
{
// initialize the aTable uniformly (run this before running em_with_tricks)
int n=0;
sentPair sent ;
sHandler1.rewind();
while(sHandler1.getNextSentence(sent)) {
Vector<WordIndex>& es = sent.eSent;
Vector<WordIndex>& fs = sent.fSent;
WordIndex l = es.size() - 1;
WordIndex m = fs.size() - 1;
n++;
if(1<=m&&aTable.getValue(l,m,l,m)<=PROB_SMOOTH) {
PROB uniform_val = 1.0 / (l+1) ;
for(WordIndex j=1; j <= m; j++)
for(WordIndex i=0; i <= l; i++)
aTable.setValue(i,j, l, m, uniform_val);
}
}
}
int model2::em_with_tricks(int noIterations,bool dumpCount,
const char* dumpCountName, bool useString)
{
double minErrors=1.0;
int minIter=0;
string modelName="Model2",shortModelName="2";
time_t it_st, st, it_fn, fn;
string tfile, afile, number, alignfile, test_alignfile;
int pair_no = 0;
bool dump_files = false ;
ofstream of2 ;
st = time(NULL) ;
sHandler1.rewind();
cout << "\n==========================================================\n";
cout << modelName << " Training Started at: " << my_ctime(&st) << " iter: " << noIterations << "\n";
for(int it=1; it <= noIterations ; it++) {
pair_no = 0;
it_st = time(NULL) ;
cout << endl << "-----------\n" << modelName << ": Iteration " << it << '\n';
dump_files = (Model2_Dump_Freq != 0) && ((it % Model2_Dump_Freq) == 0) && !NODUMPS;
number = "";
int n = it;
do {
number.insert((size_t)0, 1, (char)(n % 10 + '0'));
} while((n /= 10) > 0);
tfile = Prefix + ".t" + shortModelName + "." + number ;
afile = Prefix + ".a" + shortModelName + "." + number ;
alignfile = Prefix + ".A" + shortModelName + "." + number ;
test_alignfile = Prefix + ".tst.A" + shortModelName + "." + number ;
aCountTable.clear();
initAL();
em_loop(perp, sHandler1, dump_files, alignfile.c_str(), trainViterbiPerp, false);
if( errorsAL()<minErrors ) {
minErrors=errorsAL();
minIter=it;
}
if (testPerp && testHandler)
em_loop(*testPerp, *testHandler, dump_files, test_alignfile.c_str(), *testViterbiPerp, true);
if (dump_files&&OutputInAachenFormat==1)
tTable.printCountTable(tfile.c_str(),Elist.getVocabList(),Flist.getVocabList(),1);
if(dumpCount && it == noIterations) {
string realTableName = dumpCountName;
realTableName += ".t.count";
tTable.printCountTable(realTableName.c_str(),Elist.getVocabList(),Flist.getVocabList(),useString);
string realATableName = dumpCountName;
realATableName += ".a.count";
aCountTable.printRealTable(realATableName.c_str());
}
tTable.normalizeTable(Elist, Flist);
aCountTable.normalize(aTable);
cout << modelName << ": ("<<it<<") TRAIN CROSS-ENTROPY " << perp.cross_entropy()
<< " PERPLEXITY " << perp.perplexity() << '\n';
if (testPerp && testHandler)
cout << modelName << ": ("<<it<<") TEST CROSS-ENTROPY " << (*testPerp).cross_entropy()
<< " PERPLEXITY " << (*testPerp).perplexity()
<< '\n';
cout << modelName << ": ("<<it<<") VITERBI TRAIN CROSS-ENTROPY " << trainViterbiPerp.cross_entropy()
<< " PERPLEXITY " << trainViterbiPerp.perplexity() << '\n';
if (testPerp && testHandler)
cout << modelName << ": ("<<it<<") VITERBI TEST CROSS-ENTROPY " << testViterbiPerp->cross_entropy()
<< " PERPLEXITY " << testViterbiPerp->perplexity()
<< '\n';
if (dump_files) {
if(OutputInAachenFormat==0)
tTable.printProbTable(tfile.c_str(),Elist.getVocabList(),Flist.getVocabList(),OutputInAachenFormat);
aCountTable.printTable(afile.c_str());
}
it_fn = time(NULL) ;
cout << modelName << " Iteration: " << it<< " took: " << difftime(it_fn, it_st) << " seconds\n";
} // end of iterations
aCountTable.clear();
fn = time(NULL) ;
cout << endl << "Entire " << modelName << " Training took: " << difftime(fn, st) << " seconds\n";
// cout << "tTable contains " << tTable.getHash().bucket_count()
// << " buckets and " << tTable.getHash().size() << " entries." ;
cout << "==========================================================\n";
return minIter;
}
void model2::load_table(const char* aname)
{
/* This function loads the a table from the given file; use it
when you want to load results from previous a training without
doing any new training.
NAS, 7/11/99
*/
cout << "Model2: loading a table \n";
aTable.readTable(aname);
}
void model2::em_loop(Perplexity& perp, sentenceHandler& sHandler1,
bool dump_alignment, const char* alignfile, Perplexity& viterbi_perp,
bool test)
{
massert( aTable.is_distortion==0 );
massert( aCountTable.is_distortion==0 );
WordIndex i, j, l, m ;
double cross_entropy;
int pair_no=0 ;
perp.clear();
viterbi_perp.clear();
ofstream of2;
// for each sentence pair in the corpus
if (dump_alignment||FEWDUMPS )
of2.open(alignfile);
sentPair sent ;
vector<double> ferts(evlist.size());
sHandler1.rewind();
while(sHandler1.getNextSentence(sent)) {
Vector<WordIndex>& es = sent.eSent;
Vector<WordIndex>& fs = sent.fSent;
const float so = sent.getCount();
l = es.size() - 1;
m = fs.size() - 1;
cross_entropy = log(1.0);
Vector<WordIndex> viterbi_alignment(fs.size());
double viterbi_score = 1;
for(j=1; j <= m; j++) {
Vector<LpPair<COUNT,PROB> *> sPtrCache(es.size(),0); // cache pointers to table
// entries that map fs to all possible ei in this sentence.
PROB denom = 0.0;
PROB e = 0.0, word_best_score = 0;
WordIndex best_i = 0 ; // i for which fj is best maped to ei
for(i=0; i <= l; i++) {
sPtrCache[i] = tTable.getPtr(es[i], fs[j]) ;
if (sPtrCache[i] != 0 &&(*(sPtrCache[i])).prob > PROB_SMOOTH )
e = (*(sPtrCache[i])).prob * aTable.getValue(i,j, l, m) ;
else e = PROB_SMOOTH * aTable.getValue(i,j, l, m);
denom += e ;
if (e > word_best_score) {
word_best_score = e ;
best_i = i ;
}
}
viterbi_alignment[j] = best_i ;
viterbi_score *= word_best_score; ///denom ;
cross_entropy += log(denom) ;
if (denom == 0) {
if (test)
cerr << "WARNING: denom is zero (TEST)\n";
else
cerr << "WARNING: denom is zero (TRAIN)\n";
}
if (!test) {
if(denom > 0) {
COUNT val = COUNT(so) / (COUNT) double(denom) ;
for( i=0; i <= l; i++) {
PROB e(0.0);
if (sPtrCache[i] != 0 && (*(sPtrCache[i])).prob > PROB_SMOOTH)
e = (*(sPtrCache[i])).prob ;
else e = PROB_SMOOTH ;
e *= aTable.getValue(i,j, l, m);
COUNT temp = COUNT(e) * val ;
if( NoEmptyWord==0 || i!=0 )
if (sPtrCache[i] != 0)
(*(sPtrCache[i])).count += temp ;
else
tTable.incCount(es[i], fs[j], temp);
aCountTable.addValue(i,j, l, m,temp) ;
} /* end of for i */
} // end of if (denom > 0)
}// if (!test)
} // end of for (j) ;
sHandler1.setProbOfSentence(sent,cross_entropy);
perp.addFactor(cross_entropy, so, l, m,1);
viterbi_perp.addFactor(log(viterbi_score), so, l, m,1);
if (dump_alignment||(FEWDUMPS&&sent.sentenceNo<1000) )
printAlignToFile(es, fs, Elist.getVocabList(), Flist.getVocabList(), of2, viterbi_alignment, sent.sentenceNo, viterbi_score);
addAL(viterbi_alignment,sent.sentenceNo,l);
pair_no++;
} /* of while */
sHandler1.rewind();
perp.record("Model2");
viterbi_perp.record("Model2");
errorReportAL(cout,"IBM-2");
}
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