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/*
* Data.cpp
* met - Minimum Error Training
*
* Created by Nicola Bertoldi on 13/05/08.
*
*/
#include <algorithm>
#include "util/check.hh"
#include <cmath>
#include <fstream>
#include "Data.h"
#include "FileStream.h"
#include "Scorer.h"
#include "ScorerFactory.h"
#include "Util.h"
Data::Data(Scorer& ptr, const std::string& sparseweightsfile)
: theScorer(&ptr),
score_type(theScorer->getName()),
number_of_scores(0),
scoredata(new ScoreData(*theScorer)),
featdata(new FeatureData)
{
// TRACE_ERR("Data::score_type " << score_type << std::endl);
// TRACE_ERR("Data::Scorer type from Scorer: " << theScorer->getName() << endl);
if (sparseweightsfile.size()) {
sparse_weights.load(sparseweightsfile);
ostringstream msg;
msg << "Data::sparse_weights {";
sparse_weights.write(msg,"=");
msg << "}";
TRACE_ERR(msg.str() << std::endl);
}
}
Data::~Data() {
if (featdata) {
delete featdata;
featdata = NULL;
}
if (scoredata) {
delete scoredata;
scoredata = NULL;
}
}
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(ConvertStringToFeatureStatsType(subsubstring));
}
// 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()) );
}
}
//cerr << "number of sparse features: " << featentry.getSparse().size() << endl;
featdata->add(featentry,sentence_index);
}
inp.close();
}
void Data::createShards(size_t shard_count, float shard_size, const string& scorerconfig,
std::vector<Data>& shards)
{
CHECK(shard_count);
CHECK(shard_size >= 0);
CHECK(shard_size <= 1);
size_t data_size = scoredata->size();
CHECK(data_size == featdata->size());
shard_size *= data_size;
for (size_t shard_id = 0; shard_id < shard_count; ++shard_id) {
vector<size_t> shard_contents;
if (shard_size == 0) {
//split into roughly equal size shards
size_t shard_start = floor(0.5 + shard_id * (float)data_size / shard_count);
size_t shard_end = floor(0.5 + (shard_id+1) * (float)data_size / shard_count);
for (size_t i = shard_start; i < shard_end; ++i) {
shard_contents.push_back(i);
}
} else {
//create shards by randomly sampling
for (size_t i = 0; i < floor(shard_size+0.5); ++i) {
shard_contents.push_back(rand() % data_size);
}
}
Scorer* scorer = ScorerFactory::getScorer(score_type, scorerconfig);
shards.push_back(Data(*scorer));
shards.back().score_type = score_type;
shards.back().number_of_scores = number_of_scores;
for (size_t i = 0; i < shard_contents.size(); ++i) {
shards.back().featdata->add(featdata->get(shard_contents[i]));
shards.back().scoredata->add(scoredata->get(shard_contents[i]));
}
//cerr << endl;
}
}
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