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#include <iostream>
#include <vector>
#include <boost/program_options.hpp>
#include <boost/functional/hash.hpp>
#ifndef HAVE_OLD_CPP
# include <unordered_map>
#else
# include <tr1/unordered_map>
namespace std { using std::tr1::unordered_map; }
#endif
#include "prob.h"
#include "tdict.h"
#include "ns.h"
#include "filelib.h"
#include "stringlib.h"
using namespace std;
namespace po = boost::program_options;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("scale,a",po::value<double>()->default_value(1.0), "Posterior scaling factor (alpha)")
("evaluation_metric,m",po::value<string>()->default_value("ibm_bleu"), "Evaluation metric")
("input,i",po::value<string>()->default_value("-"), "File to read k-best lists from")
("output_list,L", "Show reranked list as output")
("help,h", "Help");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
bool flag = false;
if (flag || conf->count("help")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
struct ScoreComparer {
bool operator()(const pair<vector<WordID>, prob_t>& a, const pair<vector<WordID>, prob_t>& b) const {
return a.second > b.second;
}
};
struct LossComparer {
bool operator()(const pair<vector<WordID>, prob_t>& a, const pair<vector<WordID>, prob_t>& b) const {
return a.second < b.second;
}
};
bool ReadKBestList(const double mbr_scale, istream* in, string* sent_id, vector<pair<vector<WordID>, prob_t> >* list) {
static string cache_id;
static pair<vector<WordID>, prob_t> cache_pair;
list->clear();
string cur_id;
unordered_map<vector<WordID>, unsigned, boost::hash<vector<WordID> > > sent2id;
if (cache_pair.first.size() > 0) {
list->push_back(cache_pair);
sent2id[cache_pair.first] = 0;
cur_id = cache_id;
cache_pair.first.clear();
}
string line;
string tstr;
while(getline(*in, line)) {
size_t p1 = line.find(" ||| ");
if (p1 == string::npos) { cerr << "Bad format: " << line << endl; abort(); }
size_t p2 = line.find(" ||| ", p1 + 4);
if (p2 == string::npos) { cerr << "Bad format: " << line << endl; abort(); }
size_t p3 = line.rfind(" ||| ");
cache_id = line.substr(0, p1);
tstr = line.substr(p1 + 5, p2 - p1 - 5);
double val = strtod(line.substr(p3 + 5).c_str(), NULL) * mbr_scale;
TD::ConvertSentence(tstr, &cache_pair.first);
cache_pair.second.logeq(val);
if (cur_id.empty()) cur_id = cache_id;
if (cur_id == cache_id) {
unordered_map<vector<WordID>, unsigned, boost::hash<vector<WordID> > >::iterator it =
sent2id.find(cache_pair.first);
if (it == sent2id.end()) {
sent2id.insert(make_pair(cache_pair.first, unsigned(list->size())));
list->push_back(cache_pair);
} else {
(*list)[it->second].second += cache_pair.second;
// cerr << "Cruch: " << line << "\n newp=" << (*list)[it->second].second << endl;
}
*sent_id = cur_id;
cache_pair.first.clear();
} else { break; }
}
sort(list->begin(), list->end(), ScoreComparer());
return !list->empty();
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const string smetric = conf["evaluation_metric"].as<string>();
EvaluationMetric* metric = EvaluationMetric::Instance(smetric);
const bool is_loss = (UppercaseString(smetric) == "TER");
const bool output_list = conf.count("output_list") > 0;
const string file = conf["input"].as<string>();
const double mbr_scale = conf["scale"].as<double>();
cerr << "Posterior scaling factor (alpha) = " << mbr_scale << endl;
vector<pair<vector<WordID>, prob_t> > list;
ReadFile rf(file);
string sent_id;
while(ReadKBestList(mbr_scale, rf.stream(), &sent_id, &list)) {
vector<prob_t> joints(list.size());
const prob_t max_score = list.front().second;
prob_t marginal = prob_t::Zero();
for (int i = 0 ; i < list.size(); ++i) {
const prob_t joint = list[i].second / max_score;
joints[i] = joint;
//cerr << "list[" << i << "] joint=" << log(joint) << endl;
marginal += joint;
}
int mbr_idx = -1;
vector<double> mbr_scores(output_list ? list.size() : 0);
double mbr_loss = numeric_limits<double>::max();
for (int i = 0 ; i < list.size(); ++i) {
const vector<vector<WordID> > refs(1, list[i].first);
boost::shared_ptr<SegmentEvaluator> segeval = metric->
CreateSegmentEvaluator(refs);
double wl_acc = 0;
for (int j = 0; j < list.size(); ++j) {
if (i != j) {
SufficientStats ss;
segeval->Evaluate(list[j].first, &ss);
double loss = 1.0 - metric->ComputeScore(ss);
if (is_loss) loss = 1.0 - loss;
double weighted_loss = loss * (joints[j] / marginal).as_float();
wl_acc += weighted_loss;
if ((!output_list) && wl_acc > mbr_loss) break;
}
}
if (output_list) mbr_scores[i] = wl_acc;
if (wl_acc < mbr_loss) {
mbr_loss = wl_acc;
mbr_idx = i;
}
}
// cerr << "ML translation: " << TD::GetString(list[0].first) << endl;
cerr << "MBR Best idx: " << mbr_idx << endl;
if (output_list) {
for (int i = 0; i < list.size(); ++i)
list[i].second.logeq(mbr_scores[i]);
sort(list.begin(), list.end(), LossComparer());
for (int i = 0; i < list.size(); ++i)
cout << sent_id << " ||| "
<< TD::GetString(list[i].first) << " ||| "
<< log(list[i].second) << endl;
} else {
cout << TD::GetString(list[mbr_idx].first) << endl;
}
}
return 0;
}
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