LanguageModel::LanguageModel(const std::string & filename, ClassEncoder & encoder, ClassDecoder * classdecoder, bool debug) {
this->DEBUG = debug;
this->classdecoder = classdecoder;
order = 0;
bool hasunk = false;
ifstream f;
f.open(filename.c_str(), ios::in);
if ((!f) || (!f.good())) {
cerr << "File does not exist: " << filename << endl;
exit(3);
}
while (!f.eof()) {
string line;
getline(f, line);
if (line == "\\data\\") {
order = 0;
} else if (line == "\\1-grams:") { //bit inelegant, but simplest
order = 1;
} else if (line == "\\2-grams:") {
order = 2;
} else if (line == "\\3-grams:") {
order = 3;
} else if (line == "\\4-grams:") {
order = 4;
} else if (line == "\\5-grams:") {
order = 5;
} else if (line == "\\6-grams:") {
order = 6;
} else if (line == "\\7-grams:") {
order = 7;
} else if (line == "\\8-grams:") {
order = 8;
} else if (line == "\\9-grams:") {
order = 9;
} else if (!line.empty()) {
if (order == 0) {
if (line.substr(0,5) == "ngram") {
string n_s = line.substr(6,1);
string v_s = line.substr(8);
int n = atoi(n_s.c_str());
int v = atoi(v_s.c_str());
total[n] = v;
}
} else if (order > 0) {
string logprob_s = "";
string backofflogprob_s = "";
string ngramcontent = "";
int fields = 0;
int begin = 0;
for (unsigned int i = 0; i <= line.length(); i++) {
if ((line[i] == '\t') || (line[i] == '\n') || (i == line.length())) {
if (fields == 0) {
logprob_s = line.substr(begin, i - begin);
} else if (fields == 1) {
ngramcontent = line.substr(begin, i - begin);
} else if (fields == 2) {
backofflogprob_s = line.substr(begin, i - begin);
}
begin = i + 1;
fields++;
}
}
if ((!logprob_s.empty()) && (!ngramcontent.empty())) {
if (ngramcontent == "<unk>") {
ngrams[UNKPATTERN] = atof(logprob_s.c_str()) * log(10); //* log(10) does log10 to log_e conversion
hasunk = true;
if (DEBUG) {
cerr << " Adding UNKNOWN to LM: " << (int) UNKPATTERN.n() << "\t" << ngramcontent << "\t" << ngrams[UNKPATTERN] << endl;
}
} else {
Pattern ngram = encoder.buildpattern(ngramcontent);
if (ngram != UNKPATTERN) {
ngrams[ngram] = atof(logprob_s.c_str()) * log(10); //* log(10) does log10 to log_e conversion
if (!backofflogprob_s.empty()) {
backoff[ngram] = atof(backofflogprob_s.c_str()) * log(10); //* log(10) does log10 to log_e conversion
if (DEBUG) cerr << " Adding to LM: " << (int) ngram.n() << "\t" << ngramcontent << "\t" << ngrams[ngram] << "\t" << backoff[ngram] << endl;
} else {
if (DEBUG) cerr << " Adding to LM: " << (int) ngram.n() << "\t" << ngramcontent << "\t" << ngrams[ngram] << endl;
}
}
}
} else {
cerr << "WARNING: Ignoring line: " << line << endl;
}
} else {
cerr << "WARNING: Don't know what to do with line: " << line << endl;
}
}
}
f.close();
if (!hasunk) {
cerr << "ERROR: Language Model has no value <unk>, make sure to generate SRILM model with -unk parameter" << endl;
exit(3);
}
}
void loadmosesphrasetable(PatternAlignmentModel<double> & model, const std::string & filename, ClassEncoder & sourceencoder, ClassEncoder & targetencoder, PatternSetModel * constrainsourcemodel = NULL, PatternSetModel * constraintargetmodel = NULL, int max_sourcen =0, const double pts=0, const double pst=0, const double joinedthreshold=0, const double divergencefrombestthreshold=0.0, const std::string delimiter = "|||", const int score_column=3, const int pstfield = 0, const int ptsfield=2, const int maxscores = 10)
{
unsigned int added = 0;
unsigned int skipped = 0;
unsigned int constrained = 0;
unsigned int count = 0;
PatternSetModel firstwords;
if (constrainsourcemodel != NULL) {
cerr << "(Inferring extra contraints from source model, for faster discarding of patterns)" << endl;
for (PatternSetModel::iterator iter = constrainsourcemodel->begin(); iter != constrainsourcemodel->end(); iter++) {
const Pattern pattern = *iter;
const Pattern firstword = pattern[0];
firstwords.insert(firstword);
}
cerr << "(added " << firstwords.size() << " unigrams)" << endl;
}
//load from moses-style phrasetable file
istream * f = NULL;
if (filename.substr(filename.size()-3) == ".gz") {
cerr << "(Reading from gzip)" << endl;
f = new igzstream(filename.c_str(), ios::in | ios::binary);
} else {
f = new ifstream(filename.c_str(), ios::in | ios::binary);
}
if ((f == NULL) || (!f->good())) {
cerr << "File does not exist: " << filename << endl;
exit(2);
}
vector<BufferItem> buffer;
string firstword;
bool skipsamesource = false;
string prevsource;
string skipfirstword;
string source = "";
string target = "";
string scores_s;
bool abort = false;
int mode = 0;
int begin = 0;
bool updated = false;
string line;
vector<double> scores;
while (!f->eof()) {
line.clear();
getline(*f, line);
count++;
if (count % 100000 == 0) {
cerr << endl;
cerr << "Loading and encoding phrase-table: @" << count << " total added: " << added << ", skipped because of threshold: " << skipped << ", skipped because of constraints: " << constrained;
}
if (count % 1000 == 0) {
if (updated) {
cerr << ":";
} else {
cerr << ".";
}
updated = false;
}
mode = 0;
abort = false;
begin = 0;
source.clear();
target.clear();
scores_s.clear();
const int linesize = line.size();
if (linesize == 0) continue;
for (unsigned int i = 0; i < linesize; i++) {
if (line.substr(i,5) == " ||| ") {
if (mode == 0) {
source = line.substr(begin, i - begin);
int j = 0;
firstword = source;
for (auto c : source) {
if (c == ' ') {
firstword = source.substr(0,j);
break;
}
j++;
}
if (firstword == skipfirstword) {
abort = true;
break;
}
} else if (mode == 1) {
target = line.substr(begin, i - begin);
} else if (mode == 2) {
scores_s = line.substr(begin, i - begin);
}
begin = i+5;
mode++;
}
}
if (mode == 2) {
scores_s = line.substr(begin);
mode++;
}
if ((abort) || (firstword == skipfirstword)) {
constrained++;
continue;
} else if ((skipsamesource) && (source == prevsource)) {
constrained++;
continue;
} else {
skipsamesource = false;
skipfirstword = "";
}
if (mode < 3) {
cerr << endl << "WARNING: Error in input format, line " << count << " (length=" << linesize << "): " << endl;
cerr << line << endl;
cerr << "SKIPPING..." << endl;
}
scores.clear();
scores_s = scores_s + " ";
begin = 0;
//cerr << "DEBUG: scores_s=" << scores_s << endl;
for (unsigned int i = 0; i < scores_s.size(); i++) {
if ((scores_s[i] == ' ') && (i > begin)) {
double score = atof(scores_s.substr(begin, i - begin).c_str());
//cerr << scores_s.substr(begin, i - begin) << " -> " << score << endl;
scores.push_back(score);
begin = i + 1;
}
}
if (((!buffer.empty()) && (source != prevsource))) {
if (buffer.size() >= 100) {
cerr << "!";
}
if (divergencefrombestthreshold > 0) {
double bestscore = 0;
for (auto bufferitem : buffer) {
if (bufferitem.scores[ptsfield] > bestscore) bestscore = bufferitem.scores[ptsfield];
}
double p = bestscore * divergencefrombestthreshold;
for (auto bufferitem : buffer) {
if (bufferitem.scores[ptsfield] >= p) {
model.add( bufferitem.sourcepattern, bufferitem.targetpattern, bufferitem.scores, false );
added++;
updated = true;
} else {
skipped++;
}
}
} else {
for (auto bufferitem : buffer) {
model.add( bufferitem.sourcepattern, bufferitem.targetpattern, bufferitem.scores, false );
added++;
updated = true;
}
}
buffer.clear();
}
//check score threshold
if ( ((pst > 0) && (scores[pstfield] < pst))
|| ((pts > 0) && (scores[ptsfield] < pts))
|| ((joinedthreshold > 0) && (scores[ptsfield] * scores[pstfield] < joinedthreshold))
) {
skipped++;
} else {
//add to phrasetable
try{
Pattern sourcepattern = sourceencoder.buildpattern(source);
if ((constrainsourcemodel != NULL) && (!constrainsourcemodel->has(sourcepattern))) {
const int _n = sourcepattern.n();
if (_n == 1) {
skipfirstword = firstword;
} else if (!firstwords.has(sourcepattern[0])) {
skipfirstword = firstword;
}
constrained++;
skipsamesource = true;
continue;
}
if ((max_sourcen > 0) && (sourcepattern.n() > max_sourcen)) {
skipped++;
skipsamesource = true;
continue;
}
Pattern targetpattern = targetencoder.buildpattern(target);
if ((constraintargetmodel != NULL) && (!constraintargetmodel->has(targetpattern))) {
constrained++;
continue;
}
if (scores.size() > maxscores) {
cerr << endl << "*** WARNING: Unexpectedly large number of scores in line " << count << ", something wrong? ***" << endl;
}
buffer.push_back( BufferItem(sourcepattern, targetpattern, scores) );
} catch (const UnknownTokenError &e) {
cerr << endl << "*** WARNING: UnknownTokenError in encoding of source or target fragment on line " << count << " skipping: " << source << " ||| " << target <<endl;
}
}
}
//don't forget last one in buffer:
if (!buffer.empty()) {
if (divergencefrombestthreshold > 0) {
double bestscore = 0;
for (auto bufferitem : buffer) if (bufferitem.scores[ptsfield] > bestscore) bestscore = bufferitem.scores[ptsfield];
double p = bestscore * divergencefrombestthreshold;
for (auto bufferitem : buffer) {
if (bufferitem.scores[ptsfield] >= p) {
model.add( bufferitem.sourcepattern, bufferitem.targetpattern, bufferitem.scores, false );
added++;
} else {
skipped++;
}
}
} else {
for (auto bufferitem : buffer) {
model.add( bufferitem.sourcepattern, bufferitem.targetpattern, bufferitem.scores,false );
added++;
}
}
buffer.clear();
}
cerr << "Read " << count << " lines" << endl;
cerr << "Added: " << added << " -- skipped due to threshold: " << skipped << " -- skipped by constraint: " << constrained << endl;
cerr << "Source patterns: " << model.size() << endl;
}
