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#include <sstream>
#include <iostream>
#include <vector>
#include <cassert>
#include <cmath>
#include "config.h"
#include <boost/shared_ptr.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "sentence_metadata.h"
#include "scorer.h"
#include "verbose.h"
#include "viterbi.h"
#include "hg.h"
#include "prob.h"
#include "kbest.h"
#include "ff_register.h"
#include "decoder.h"
#include "filelib.h"
#include "fdict.h"
#include "weights.h"
#include "sparse_vector.h"
using namespace std;
using boost::shared_ptr;
namespace po = boost::program_options;
void SanityCheck(const vector<double>& w) {
for (int i = 0; i < w.size(); ++i) {
assert(!isnan(w[i]));
assert(!isinf(w[i]));
}
}
struct FComp {
const vector<double>& w_;
FComp(const vector<double>& w) : w_(w) {}
bool operator()(int a, int b) const {
return fabs(w_[a]) > fabs(w_[b]);
}
};
void ShowLargestFeatures(const vector<double>& w) {
vector<int> fnums(w.size());
for (int i = 0; i < w.size(); ++i)
fnums[i] = i;
vector<int>::iterator mid = fnums.begin();
mid += (w.size() > 10 ? 10 : w.size());
partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
cerr << "TOP FEATURES:";
for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
}
cerr << endl;
}
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("input_weights,w",po::value<string>(),"Input feature weights file")
("source,i",po::value<string>(),"Source file for development set")
("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)")
("mt_metric,m",po::value<string>()->default_value("ter"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)")
("max_step_size,C", po::value<double>()->default_value(0.0001), "maximum step size (C)")
("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by")
("decoder_config,c",po::value<string>(),"Decoder configuration file");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
("help,h", "Print this help message and exit");
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (conf->count("config")) {
ifstream config((*conf)["config"].as<string>().c_str());
po::store(po::parse_config_file(config, dconfig_options), *conf);
}
po::notify(*conf);
if (conf->count("help") || !conf->count("input_weights") || !conf->count("source") || !conf->count("decoder_config") || !conf->count("reference")) {
cerr << dcmdline_options << endl;
return false;
}
return true;
}
static const double kMINUS_EPSILON = -1e-6;
struct HypothesisInfo {
SparseVector<double> features;
double mt_metric;
};
struct GoodBadOracle {
shared_ptr<HypothesisInfo> good;
shared_ptr<HypothesisInfo> bad;
};
struct TrainingObserver : public DecoderObserver {
TrainingObserver(const DocScorer& d, vector<GoodBadOracle>* o) : ds(d), oracles(*o) {}
const DocScorer& ds;
vector<GoodBadOracle>& oracles;
shared_ptr<HypothesisInfo> cur_best;
const HypothesisInfo& GetCurrentBestHypothesis() const {
return *cur_best;
}
virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
UpdateOracles(smeta.GetSentenceID(), *hg);
}
shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) {
shared_ptr<HypothesisInfo> h(new HypothesisInfo);
h->features = feats;
h->mt_metric = score;
return h;
}
void UpdateOracles(int sent_id, const Hypergraph& forest) {
int kbest_size = 330;
shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good;
shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad;
cur_bad.reset(); // TODO get rid of??
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size);
for (int i = 0; i < kbest_size; ++i) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
kbest.LazyKthBest(forest.nodes_.size() - 1, i);
if (!d) break;
float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore();
// cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl;
if (i == 0)
cur_best = MakeHypothesisInfo(d->feature_values, sentscore);
if (!cur_good || sentscore < cur_good->mt_metric)
cur_good = MakeHypothesisInfo(d->feature_values, sentscore);
if (!cur_bad || sentscore > cur_bad->mt_metric)
cur_bad = MakeHypothesisInfo(d->feature_values, sentscore);
}
cerr << "GOOD: " << cur_good->mt_metric << endl;
cerr << " BAD: " << cur_bad->mt_metric << endl;
cerr << " #1: " << cur_best->mt_metric << endl;
}
};
void ReadTrainingCorpus(const string& fname, vector<string>* c) {
ReadFile rf(fname);
istream& in = *rf.stream();
string line;
while(in) {
getline(in, line);
if (!in) break;
c->push_back(line);
}
}
bool ApproxEqual(double a, double b) {
if (a == b) return true;
return (fabs(a-b)/fabs(b)) < 0.000001;
}
int main(int argc, char** argv) {
register_feature_functions();
//SetSilent(true); // turn off verbose decoder output
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
vector<string> corpus;
ReadTrainingCorpus(conf["source"].as<string>(), &corpus);
const string metric_name = conf["mt_metric"].as<string>();
ScoreType type = ScoreTypeFromString(metric_name);
DocScorer ds(type, conf["reference"].as<vector<string> >(), "");
cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl;
if (ds.size() != corpus.size()) {
cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n";
return 1;
}
// load initial weights
Weights weights;
weights.InitFromFile(conf["input_weights"].as<string>());
SparseVector<double> lambdas;
weights.InitSparseVector(&lambdas);
// freeze feature set (should be optional?)
const bool freeze_feature_set = true;
if (freeze_feature_set) FD::Freeze();
ReadFile ini_rf(conf["decoder_config"].as<string>());
Decoder decoder(ini_rf.stream());
const double max_step_size = conf["max_step_size"].as<double>();
const double mt_metric_scale = conf["mt_metric_scale"].as<double>();
assert(corpus.size() > 0);
vector<GoodBadOracle> oracles(corpus.size());
TrainingObserver observer(ds, &oracles);
int cur_sent = 0;
bool converged = false;
vector<double> dense_weights;
while (!converged) {
dense_weights.clear();
weights.InitFromVector(lambdas);
weights.InitVector(&dense_weights);
decoder.SetWeights(dense_weights);
if (corpus.size() == cur_sent) cur_sent = 0;
decoder.SetId(cur_sent);
decoder.Decode(corpus[cur_sent], &observer); // update oracles
const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis();
const HypothesisInfo& cur_good = *oracles[cur_sent].good;
const HypothesisInfo& cur_bad = *oracles[cur_sent].bad;
if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) {
const double loss = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights) +
mt_metric_scale * (cur_good.mt_metric - cur_bad.mt_metric);
cerr << "LOSS: " << loss << endl;
if (loss > 0.0) {
SparseVector<double> diff = cur_good.features;
diff -= cur_bad.features;
double step_size = loss / diff.l2norm_sq();
//cerr << loss << " " << step_size << " " << diff << endl;
if (step_size > max_step_size) step_size = max_step_size;
lambdas += (cur_good.features * step_size);
lambdas -= (cur_bad.features * step_size);
//cerr << "L: " << lambdas << endl;
}
}
++cur_sent;
static int cc = 0; ++cc; if (cc==250) converged = true;
}
weights.WriteToFile("-");
return 0;
}
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