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-rw-r--r--decoder/decoder.cc2
-rw-r--r--mira/kbest_mira.cc76
-rw-r--r--mteval/scorer.cc8
-rw-r--r--utils/sparse_vector.h6
4 files changed, 64 insertions, 28 deletions
diff --git a/decoder/decoder.cc b/decoder/decoder.cc
index d8dd0c61..ff068be9 100644
--- a/decoder/decoder.cc
+++ b/decoder/decoder.cc
@@ -942,7 +942,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
} else if (csplit_output_plf) {
cout << HypergraphIO::AsPLF(forest, false) << endl;
} else {
- if (!graphviz && !has_ref && !joshua_viz) {
+ if (!graphviz && !has_ref && !joshua_viz && !SILENT) {
vector<WordID> trans;
ViterbiESentence(forest, &trans);
cout << TD::GetString(trans) << endl << flush;
diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc
index 7ff207a8..c0c39232 100644
--- a/mira/kbest_mira.cc
+++ b/mira/kbest_mira.cc
@@ -28,6 +28,8 @@ using namespace std;
using boost::shared_ptr;
namespace po = boost::program_options;
+bool invert_score;
+
void SanityCheck(const vector<double>& w) {
for (int i = 0; i < w.size(); ++i) {
assert(!isnan(w[i]));
@@ -62,10 +64,12 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
opts.add_options()
("input_weights,w",po::value<string>(),"Input feature weights file")
("source,i",po::value<string>(),"Source file for development set")
+ ("passes,p", po::value<int>()->default_value(15), "Number of passes through the training data")
("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,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)")
+ ("max_step_size,C", po::value<double>()->default_value(0.001), "maximum step size (C)")
("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by")
+ ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to evaluate")
("decoder_config,c",po::value<string>(),"Decoder configuration file");
po::options_description clo("Command line options");
clo.add_options()
@@ -102,10 +106,11 @@ struct GoodBadOracle {
};
struct TrainingObserver : public DecoderObserver {
- TrainingObserver(const DocScorer& d, vector<GoodBadOracle>* o) : ds(d), oracles(*o) {}
+ TrainingObserver(const int k, const DocScorer& d, vector<GoodBadOracle>* o) : ds(d), oracles(*o), kbest_size(k) {}
const DocScorer& ds;
vector<GoodBadOracle>& oracles;
shared_ptr<HypothesisInfo> cur_best;
+ const int kbest_size;
const HypothesisInfo& GetCurrentBestHypothesis() const {
return *cur_best;
@@ -123,7 +128,6 @@ struct TrainingObserver : public DecoderObserver {
}
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??
@@ -133,17 +137,18 @@ struct TrainingObserver : public DecoderObserver {
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 (invert_score) sentscore *= -1.0;
+ // 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)
+ if (!cur_good || sentscore > cur_good->mt_metric)
cur_good = MakeHypothesisInfo(d->feature_values, sentscore);
- if (!cur_bad || sentscore > cur_bad->mt_metric)
+ 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;
+ //cerr << "GOOD: " << cur_good->mt_metric << endl;
+ //cerr << " CUR: " << cur_best->mt_metric << endl;
+ //cerr << " BAD: " << cur_bad->mt_metric << endl;
}
};
@@ -165,7 +170,7 @@ bool ApproxEqual(double a, double b) {
int main(int argc, char** argv) {
register_feature_functions();
- //SetSilent(true); // turn off verbose decoder output
+ SetSilent(true); // turn off verbose decoder output
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
@@ -174,6 +179,11 @@ int main(int argc, char** argv) {
ReadTrainingCorpus(conf["source"].as<string>(), &corpus);
const string metric_name = conf["mt_metric"].as<string>();
ScoreType type = ScoreTypeFromString(metric_name);
+ if (type == TER) {
+ invert_score = true;
+ } else {
+ invert_score = false;
+ }
DocScorer ds(type, conf["reference"].as<vector<string> >(), "");
cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl;
if (ds.size() != corpus.size()) {
@@ -186,10 +196,6 @@ int main(int argc, char** argv) {
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>();
@@ -198,25 +204,46 @@ int main(int argc, char** argv) {
assert(corpus.size() > 0);
vector<GoodBadOracle> oracles(corpus.size());
- TrainingObserver observer(ds, &oracles);
+ TrainingObserver observer(conf["k_best_size"].as<int>(), ds, &oracles);
int cur_sent = 0;
+ int lcount = 0;
+ double tot_loss = 0;
+ int dots = 0;
+ int cur_pass = 0;
bool converged = false;
vector<double> dense_weights;
- while (!converged) {
+ SparseVector<double> tot;
+ tot += lambdas; // initial weights
+ lcount++; // count for initial weights
+ int max_iteration = conf["passes"].as<int>() * corpus.size();
+ string msg = "# MIRA tuned weights";
+ while (lcount <= max_iteration) {
dense_weights.clear();
weights.InitFromVector(lambdas);
weights.InitVector(&dense_weights);
decoder.SetWeights(dense_weights);
- if (corpus.size() == cur_sent) cur_sent = 0;
+ if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
+ if (corpus.size() == cur_sent) {
+ cur_sent = 0;
+ cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
+ tot_loss = 0;
+ dots = 0;
+ ostringstream os;
+ os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz";
+ weights.WriteToFile(os.str(), true, &msg);
+ ++cur_pass;
+ }
+ if (cur_sent == 0) { cerr << "PASS " << (lcount / corpus.size() + 1) << endl << lambdas << endl; }
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;
+ tot_loss += cur_hyp.mt_metric;
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;
+ //cerr << "LOSS: " << loss << endl;
if (loss > 0.0) {
SparseVector<double> diff = cur_good.features;
diff -= cur_bad.features;
@@ -228,10 +255,17 @@ int main(int argc, char** argv) {
//cerr << "L: " << lambdas << endl;
}
}
+ tot += lambdas;
+ ++lcount;
++cur_sent;
- static int cc = 0; ++cc; if (cc==250) converged = true;
}
- weights.WriteToFile("-");
+ cerr << endl;
+ weights.WriteToFile("weights.mira-final.gz", true, &msg);
+ tot /= lcount;
+ weights.InitFromVector(tot);
+ msg = "# MIRA tuned weights (averaged vector)";
+ weights.WriteToFile("weights.mira-final-avg.gz", true, &msg);
+ cerr << "Optimization complete.\\AVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
return 0;
}
diff --git a/mteval/scorer.cc b/mteval/scorer.cc
index 04eeaa93..64ce63af 100644
--- a/mteval/scorer.cc
+++ b/mteval/scorer.cc
@@ -410,8 +410,7 @@ void BLEUScore::ScoreDetails(string* details) const {
float bleu = ComputeScore(&precs, &bp);
for (int i=N();i<4;++i)
precs[i]=0.;
- char *bufn;
- bufn=buf+sprintf(buf, "BLEU = %.2f, %.1f|%.1f|%.1f|%.1f (brev=%.3f)",
+ sprintf(buf, "BLEU = %.2f, %.1f|%.1f|%.1f|%.1f (brev=%.3f)",
bleu*100.0,
precs[0]*100.0,
precs[1]*100.0,
@@ -427,7 +426,10 @@ float BLEUScore::ComputeScore(vector<float>* precs, float* bp) const {
int count = 0;
for (int i = 0; i < N(); ++i) {
if (hyp_ngram_counts[i] > 0) {
- float lprec = log(correct_ngram_hit_counts[i]) - log(hyp_ngram_counts[i]);
+ float cor_count = correct_ngram_hit_counts[i];
+ // smooth bleu
+ if (!cor_count) { cor_count = 0.01; }
+ float lprec = log(cor_count) - log(hyp_ngram_counts[i]);
if (precs) precs->push_back(exp(lprec));
log_bleu += lprec;
++count;
diff --git a/utils/sparse_vector.h b/utils/sparse_vector.h
index e3721b50..a55436fb 100644
--- a/utils/sparse_vector.h
+++ b/utils/sparse_vector.h
@@ -42,14 +42,14 @@ BOOST_CLASS_TRACKING(SparseVector<double>,track_never)
#include "fast_sparse_vector.h"
#define SparseVector FastSparseVector
-template <class T>
-SparseVector<T> operator*(const SparseVector<T>& a, const double& b) {
+template <class T, typename S>
+SparseVector<T> operator*(const SparseVector<T>& a, const S& b) {
SparseVector<T> result = a;
return result *= b;
}
template <class T>
-SparseVector<T> operator*(const SparseVector<T>& a, const T& b) {
+SparseVector<T> operator*(const SparseVector<T>& a, const double& b) {
SparseVector<T> result = a;
return result *= b;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-23 03:19:43 +0000