void Data::InitFeatureMap(const string& str) {
string buf = str;
string substr;
string features = "";
string tmp_name = "";
size_t tmp_index = 0;
string::size_type loc;
char tmp[64]; // for snprintf();
while (!buf.empty()) {
getNextPound(buf, substr);
// string ending with ":" are skipped, because they are the names of the features
if ((loc = substr.find_last_of(":")) != substr.length()-1) {
snprintf(tmp, sizeof(tmp), "%s_%lu ", tmp_name.c_str(), tmp_index);
features.append(tmp);
tmp_index++;
} else if (substr.find("_") != string::npos) {
// ignore sparse feature name and its value
getNextPound(buf, substr);
} else { // update current feature name
tmp_index = 0;
tmp_name = substr.substr(0, substr.size() - 1);
}
}
m_feature_data->setFeatureMap(features);
}
void Data::InitFeatureMap(const string& str)
{
string buf = str;
string substr;
string features = "";
string tmp_name = "";
size_t tmp_index = 0;
while (!buf.empty()) {
getNextPound(buf, substr);
// string ending with "=" are skipped, because they are the names of the features
if (!EndsWith(substr, "=")) {
stringstream ss;
ss << tmp_name << "_" << tmp_index << " ";
features.append(ss.str());
tmp_index++;
} else if (substr.find("_") != string::npos) {
// ignore sparse feature name and its value
getNextPound(buf, substr);
} else { // update current feature name
tmp_index = 0;
tmp_name = substr.substr(0, substr.size() - 1);
}
}
m_feature_data->setFeatureMap(features);
}
void Data::loadNBest(const string &file)
{
TRACE_ERR("loading nbest from " << file << endl);
inputfilestream inp(file); // matches a stream with a file. Opens the file
if (!inp.good())
throw runtime_error("Unable to open: " + file);
ScoreStats scoreentry;
string line, sentence_index, sentence, feature_str;
while (getline(inp, line, '\n')) {
if (line.empty()) continue;
// adding statistics for error measures
scoreentry.clear();
getNextPound(line, sentence_index, "|||"); // first field
getNextPound(line, sentence, "|||"); // second field
getNextPound(line, feature_str, "|||"); // third field
m_scorer->prepareStats(sentence_index, sentence, scoreentry);
m_score_data->add(scoreentry, sentence_index);
// examine first line for name of features
if (!existsFeatureNames()) {
InitFeatureMap(feature_str);
}
AddFeatures(feature_str, sentence_index);
}
inp.close();
}
// TODO: This is too long. Consider creating a function for
// initialization such as Init().
InterpolatedScorer::InterpolatedScorer(const string& name, const string& config)
: Scorer(name,config)
{
// name would be: HAMMING,BLEU or similar
string scorers = name;
while (scorers.length() > 0) {
string scorertype = "";
getNextPound(scorers, scorertype,",");
Scorer *scorer = ScorerFactory::getScorer(scorertype,config);
m_scorers.push_back(scorer);
}
if (m_scorers.size() == 0) {
throw runtime_error("There are no scorers");
}
cerr << "Number of scorers: " << m_scorers.size() << endl;
//TODO debug this
string wtype = getConfig("weights","");
//Default weights set to uniform ie. if two weights 0.5 each
//weights should add to 1
if (wtype.length() == 0) {
float weight = 1.0 / m_scorers.size() ;
//cout << " Default weights:" << weight << endl;
for (size_t i = 0; i < m_scorers.size(); i ++) {
m_scorer_weights.push_back(weight);
}
} else {
float tot=0;
//cout << "Defined weights:" << endl;
while (wtype.length() > 0) {
string scoreweight = "";
getNextPound(wtype,scoreweight,"+");
float weight = atof(scoreweight.c_str());
m_scorer_weights.push_back(weight);
tot += weight;
//cout << " :" << weight ;
}
//cout << endl;
if (tot != float(1)) { // TODO: fix this checking in terms of readability.
for (vector<float>::iterator it = m_scorer_weights.begin();
it != m_scorer_weights.end(); ++it) {
*it /= tot;
}
}
if (m_scorers.size() != m_scorer_weights.size()) {
throw runtime_error("The number of weights does not equal the number of scorers!");
}
}
cerr << "The weights for the interpolated scorers are: " << endl;
for (vector<float>::iterator it = m_scorer_weights.begin(); it < m_scorer_weights.end(); it++) {
cerr << *it << " " ;
}
cerr <<endl;
}
void ScoreArray::load(istream* is)
{
size_t number_of_entries = 0;
bool binmode = false;
string substring, stringBuf;
string::size_type loc;
getline(*is, stringBuf);
if (!is->good()) {
return;
}
if (!stringBuf.empty()) {
if ((loc = stringBuf.find(SCORES_TXT_BEGIN)) == 0) {
binmode=false;
} else if ((loc = stringBuf.find(SCORES_BIN_BEGIN)) == 0) {
binmode=true;
} else {
TRACE_ERR("ERROR: ScoreArray::load(): Wrong header");
return;
}
getNextPound(stringBuf, substring);
getNextPound(stringBuf, substring);
m_index = atoi(substring.c_str());
getNextPound(stringBuf, substring);
number_of_entries = atoi(substring.c_str());
getNextPound(stringBuf, substring);
m_num_scores = atoi(substring.c_str());
getNextPound(stringBuf, substring);
m_score_type = substring;
}
if (binmode) {
loadbin(is, number_of_entries);
} else {
loadtxt(is, number_of_entries);
}
getline(*is, stringBuf);
if (!stringBuf.empty()) {
if ((loc = stringBuf.find(SCORES_TXT_END)) != 0 &&
(loc = stringBuf.find(SCORES_BIN_END)) != 0) {
TRACE_ERR("ERROR: ScoreArray::load(): Wrong footer");
return;
}
}
}
void InterpolatedScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
{
stringstream buff;
string align = text;
string sentence = "";
size_t alignmentData = text.find("|||");
//Get sentence and alignment parts
if(alignmentData != string::npos) {
getNextPound(align,sentence, "|||");
}
int i = 0;
for (ScopedVector<Scorer>::iterator itsc = m_scorers.begin(); itsc != m_scorers.end(); ++itsc) {
ScoreStats tempEntry;
if ((*itsc)->useAlignment()) {
(*itsc)->prepareStats(sid, text, tempEntry);
}
else {
(*itsc)->prepareStats(sid, sentence, tempEntry);
}
if (i > 0) buff << " ";
buff << tempEntry;
i++;
}
//cout << " Scores for interpolated: " << buff << endl;
string str = buff.str();
entry.set(str);
}
void Data::AddFeatures(const string& str,
int sentence_index) {
string buf = str;
string substr;
FeatureStats feature_entry;
feature_entry.reset();
while (!buf.empty()) {
getNextPound(buf, substr);
// no ':' -> feature value that needs to be stored
if (!EndsWith(substr, ":")) {
feature_entry.add(ConvertStringToFeatureStatsType(substr));
} else if (substr.find("_") != string::npos) {
// sparse feature name? store as well
string name = substr;
getNextPound(buf, substr);
feature_entry.addSparse(name, atof(substr.c_str()));
}
}
m_feature_data->add(feature_entry, sentence_index);
}
void Data::AddFeatures(const string& str,
const string& sentence_index) {
string::size_type loc;
string buf = str;
string substr;
FeatureStats feature_entry;
feature_entry.reset();
while (!buf.empty()) {
getNextPound(buf, substr);
// no ':' -> feature value that needs to be stored
if ((loc = substr.find_last_of(":")) != substr.length()-1) {
feature_entry.add(ConvertStringToFeatureStatsType(substr));
} else if (substr.find("_") != string::npos) {
// sparse feature name? store as well
string name = substr;
getNextPound(buf, substr);
feature_entry.addSparse(name, atof(substr.c_str()));
m_sparse_flag = true;
}
}
m_feature_data->add(feature_entry, sentence_index);
}
void FeatureStats::set(string &theString)
{
string substring, stringBuf;
reset();
while (!theString.empty()) {
getNextPound(theString, substring);
// regular feature
if (substring.find(":") == string::npos) {
add(ConvertStringToFeatureStatsType(substring));
}
// sparse feature
else {
size_t separator = substring.find_last_of(":");
addSparse(substring.substr(0,separator), atof(substring.substr(separator+1).c_str()) );
}
}
}
void PermutationScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
{
//cout << "*******prepareStats" ;
//cout << text << endl;
//cout << sid << endl;
//cout << "Reference0align:" << endl;
//m_referencePerms[0][sid].dump();
string sentence = "";
string align = text;
size_t alignmentData = text.find("|||");
//Get sentence and alignment parts
if(alignmentData != string::npos) {
getNextPound(align,sentence, "|||");
} else {
align = text;
}
int translationLength = getNumberWords(sentence);
//A vector of Permutations for each sentence
vector< vector<Permutation> > nBestPerms;
float distanceValue;
//need to create permutations for each nbest line
string standardFormat = Permutation::convertMosesToStandard(align);
Permutation perm(standardFormat, m_sourceLengths[sid],translationLength);
//perm.dump();
if (m_refChoiceStrategy == REFERENCE_CHOICE_AVERAGE) {
float total = 0;
for (size_t i = 0; i < m_referencePerms.size(); ++i) {
float dist = perm.distance(m_referencePerms[i][sid], m_distanceMetric);
total += dist;
//cout << "Ref number: " << i << " distance: " << dist << endl;
}
float mean = (float)total/m_referencePerms.size();
//cout << "MultRef strategy AVERAGE: total " << total << " mean " << mean << " number " << m_referencePerms.size() << endl;
distanceValue = mean;
} else if (m_refChoiceStrategy == REFERENCE_CHOICE_CLOSEST) {
float max_val = 0;
for (size_t i = 0; i < m_referencePerms.size(); ++i) {
//look for the closest reference
float value = perm.distance(m_referencePerms[i][sid], m_distanceMetric);
//cout << "Ref number: " << i << " distance: " << value << endl;
if (value > max_val) {
max_val = value;
}
}
distanceValue = max_val;
//cout << "MultRef strategy CLOSEST: max_val " << distanceValue << endl;
} else {
throw runtime_error("Unsupported reflength strategy");
}
//SCOREROUT eg: 0.04546
ostringstream tempStream;
tempStream.precision(SCORE_PRECISION);
tempStream << distanceValue;
string str = tempStream.str();
entry.set(str);
//cout << tempStream.str();
}
PermutationScorer::PermutationScorer(const string &distanceMetric, const string &config)
:StatisticsBasedScorer(distanceMetric,config)
{
//configure regularisation
static string KEY_REFCHOICE = "refchoice";
static string REFCHOICE_AVERAGE = "average";
static string REFCHOICE_CLOSEST = "closest";
string refchoice = getConfig(KEY_REFCHOICE,REFCHOICE_CLOSEST);
if (refchoice == REFCHOICE_AVERAGE) {
m_refChoiceStrategy = REFERENCE_CHOICE_AVERAGE;
} else if (refchoice == REFCHOICE_CLOSEST) {
m_refChoiceStrategy = REFERENCE_CHOICE_CLOSEST;
} else {
throw runtime_error("Unknown reference choice strategy: " + refchoice);
}
cerr << "Using reference choice strategy: " << refchoice << endl;
if (distanceMetric.compare("HAMMING") == 0) {
m_distanceMetric = HAMMING_DISTANCE;
} else if (distanceMetric.compare("KENDALL") == 0) {
m_distanceMetric = KENDALL_DISTANCE;
}
cerr << "Using permutation distance metric: " << distanceMetric << endl;
//Get reference alignments from scconfig refalign option
static string KEY_ALIGNMENT_FILES = "refalign";
string refalign = getConfig(KEY_ALIGNMENT_FILES,"");
//cout << refalign << endl;
if (refalign.length() > 0) {
string substring;
while (!refalign.empty()) {
getNextPound(refalign, substring, "+");
m_referenceAlignments.push_back(substring);
}
}
//Get length of source sentences read in from scconfig source option
// this is essential for extractor but unneccesary for mert executable
static string KEY_SOURCE_FILE = "source";
string sourceFile = getConfig(KEY_SOURCE_FILE,"");
if (sourceFile.length() > 0) {
cerr << "Loading source sentence lengths from " << sourceFile << endl;
ifstream sourcein(sourceFile.c_str());
if (!sourcein) {
throw runtime_error("Unable to open: " + sourceFile);
}
string line;
while (getline(sourcein,line)) {
size_t wordNumber = 0;
string word;
while(!line.empty()) {
getNextPound(line, word, " ");
wordNumber++;
}
m_sourceLengths.push_back(wordNumber);
}
sourcein.close();
}
}
void PermutationScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
{
//bool debug= (verboselevel()>3); // TODO: fix verboselevel()
bool debug=false;
if (debug) {
cout << "*******prepareStats" ;
cout << text << endl;
cout << sid << endl;
cout << "Reference0align:" << endl;
m_referencePerms[0][sid].dump();
}
string sentence = "";
string align = text;
size_t alignmentData = text.find("|||");
//Get sentence and alignment parts
if(alignmentData != string::npos) {
getNextPound(align,sentence, "|||");
} else {
align = text;
}
int translationLength = getNumberWords(sentence);
//A vector of Permutations for each sentence
vector< vector<Permutation> > nBestPerms;
float distanceValue;
//need to create permutations for each nbest line
//here we check if the alignments extracted from the nbest are phrase-based or word-based, in which case no conversion is needed
bool isWordAlignment=true;
string alignCopy = align;
string align1;
getNextPound(alignCopy,align1," ");
if (align1.length() > 0) {
size_t phraseDelimeter = align1.find("=");
if(phraseDelimeter!= string::npos)
isWordAlignment=false;
}
string standardFormat = align;
if(!isWordAlignment)
standardFormat= Permutation::convertMosesToStandard(align);
if (debug) {
cerr << "Nbest alignment: " << align << endl;
cerr << "-->std alignment: " << standardFormat << endl;
}
Permutation perm(standardFormat, m_sourceLengths[sid],translationLength);
//perm.dump();
if (m_refChoiceStrategy == REFERENCE_CHOICE_AVERAGE) {
float total = 0;
for (size_t i = 0; i < m_referencePerms.size(); ++i) {
float dist = perm.distance(m_referencePerms[i][sid], m_distanceMetric);
total += dist;
//cout << "Ref number: " << i << " distance: " << dist << endl;
}
float mean = (float)total/m_referencePerms.size();
//cout << "MultRef strategy AVERAGE: total " << total << " mean " << mean << " number " << m_referencePerms.size() << endl;
distanceValue = mean;
} else if (m_refChoiceStrategy == REFERENCE_CHOICE_CLOSEST) {
float max_val = 0;
for (size_t i = 0; i < m_referencePerms.size(); ++i) {
//look for the closest reference
float value = perm.distance(m_referencePerms[i][sid], m_distanceMetric);
//cout << "Ref number: " << i << " distance: " << value << endl;
if (value > max_val) {
max_val = value;
}
}
distanceValue = max_val;
//cout << "MultRef strategy CLOSEST: max_val " << distanceValue << endl;
} else {
throw runtime_error("Unsupported reflength strategy");
}
//SCOREROUT eg: 0.04546
distanceValue*=SCORE_MULTFACT; //SCOREROUT eg: 4546 to transform float into integer
ostringstream tempStream;
tempStream.precision(0); // decimal precision not needed as score was multiplied per SCORE_MULTFACT
tempStream << std::fixed << distanceValue << " 1"; //use for final normalization over the amount of test sentences
string str = tempStream.str();
entry.set(str);
//cout << distanceValue << "=" << distanceValue << " (str:" << tempStream.str() << ")" << endl;
}
void Data::loadnbest(const std::string &file)
{
TRACE_ERR("loading nbest from " << file << std::endl);
FeatureStats featentry;
ScoreStats scoreentry;
std::string sentence_index;
inputfilestream inp(file); // matches a stream with a file. Opens the file
if (!inp.good())
throw runtime_error("Unable to open: " + file);
std::string substring, subsubstring, stringBuf;
std::string theSentence;
std::string::size_type loc;
while (getline(inp,stringBuf,'\n')) {
if (stringBuf.empty()) continue;
// TRACE_ERR("stringBuf: " << stringBuf << std::endl);
getNextPound(stringBuf, substring, "|||"); //first field
sentence_index = substring;
getNextPound(stringBuf, substring, "|||"); //second field
theSentence = substring;
// adding statistics for error measures
featentry.reset();
scoreentry.clear();
theScorer->prepareStats(sentence_index, theSentence, scoreentry);
scoredata->add(scoreentry, sentence_index);
getNextPound(stringBuf, substring, "|||"); //third field
// examine first line for name of features
if (!existsFeatureNames()) {
std::string stringsupport=substring;
std::string features="";
std::string tmpname="";
size_t tmpidx=0;
while (!stringsupport.empty()) {
// TRACE_ERR("Decompounding: " << substring << std::endl);
getNextPound(stringsupport, subsubstring);
// string ending with ":" are skipped, because they are the names of the features
if ((loc = subsubstring.find_last_of(":")) != subsubstring.length()-1) {
features+=tmpname+"_"+stringify(tmpidx)+" ";
tmpidx++;
}
// ignore sparse feature name
else if (subsubstring.find("_") != string::npos) {
// also ignore its value
getNextPound(stringsupport, subsubstring);
}
// update current feature name
else {
tmpidx=0;
tmpname=subsubstring.substr(0,subsubstring.size() - 1);
}
}
featdata->setFeatureMap(features);
}
// adding features
while (!substring.empty()) {
// TRACE_ERR("Decompounding: " << substring << std::endl);
getNextPound(substring, subsubstring);
// no ':' -> feature value that needs to be stored
if ((loc = subsubstring.find_last_of(":")) != subsubstring.length()-1) {
featentry.add(ATOFST(subsubstring.c_str()));
}
// sparse feature name? store as well
else if (subsubstring.find("_") != string::npos) {
std::string name = subsubstring;
getNextPound(substring, subsubstring);
featentry.addSparse( name, atof(subsubstring.c_str()) );
_sparse_flag = true;
}
}
//cerr << "number of sparse features: " << featentry.getSparse().size() << endl;
featdata->add(featentry,sentence_index);
}
inp.close();
}
void DataAsiya::loadNBest(const string &file)
{
TRACE_ERR("loading nbest from DataAsiya " << file << endl);
inputfilestream inp(file); // matches a stream with a file. Opens the file
if (!inp.good())
throw runtime_error("Unable to open: " + file);
ScoreStats scoreentry;
string line, sentence_index, sentence, feature_str, alignment;
AsiyaScorer* a_scorer = dynamic_cast<AsiyaScorer*>(m_scorer);
/*todo. change this loop. instead of obtaining the score for each sentence, obtain all the scores at once!*/
while (getline(inp, line, '\n')) {
if (line.empty()) continue;
// adding statistics for error measures
scoreentry.clear();
getNextPound(line, sentence_index, "|||"); // first field
getNextPound(line, sentence, "|||"); // second field
getNextPound(line, feature_str, "|||"); // third field
if (line.length() > 0) {
string temp;
getNextPound(line, temp, "|||"); //fourth field sentence score
if (line.length() > 0) {
getNextPound(line, alignment, "|||"); //fourth field only there if alignment scorer
}
}
//TODO check alignment exists if scorers need it
if (a_scorer->useAlignment()) {
sentence += "|||";
sentence += alignment;
}
// prepare stats gets all the scores for sentence_i of sentence_index
// a_scorer->addCandidateSentence(sentence_index, sentence);
a_scorer->prepareStats(atoi(sentence_index.c_str()), sentence, scoreentry);
// examine first line for name of features
if (!existsFeatureNames()) {
InitFeatureMap(feature_str);
}
AddFeatures(feature_str, atoi(sentence_index.c_str()));
}
a_scorer->doScoring();
// TRACE_ERR("before getAllScoreStats" << endl);
std::vector<std::vector <ScoreStats> > allScoreStats = a_scorer->getAllScoreStats();
for (int i = 0; i < allScoreStats.size(); ++i)
for(int j = 0; j < allScoreStats[i].size(); ++j)
{
stringstream ss;
ss << i;
m_score_data->add(allScoreStats[i][j], atoi(ss.str().c_str()));
// TRACE_ERR("allScoreStats[" << i << "].size() " << allScoreStats[i].size() << " " << allScoreStats[i][j] << endl);
}
inp.close();
// a_scorer->doScoring( m_score_data );
//score each sentence
//a_scorer->prepareStats(sentence_index, sentence, scoreentry);
// save the score for previous sentence. Do it aling with previous function
//m_score_data->add(scoreentry, sentence_index);
}
