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-rw-r--r--mira/kbest_mira.cc76
1 files changed, 55 insertions, 21 deletions
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;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-12-24 17:52:19 +0000