1 files changed, 27 insertions, 14 deletions

diff --git a/training/mira/kbest_mira.cc b/training/mira/kbest_mira.cc
index 8b7993dd..bcb261c9 100644
--- a/training/mira/kbest_mira.cc
+++ b/training/mira/kbest_mira.cc
@@ -8,9 +8,11 @@
 #include <boost/program_options.hpp>
 #include <boost/program_options/variables_map.hpp>
 
+#include "stringlib.h"
 #include "hg_sampler.h"
 #include "sentence_metadata.h"
-#include "scorer.h"
+#include "ns.h"
+#include "ns_docscorer.h"
 #include "verbose.h"
 #include "viterbi.h"
 #include "hg.h"
@@ -91,8 +93,9 @@ struct GoodBadOracle {
 };
 
 struct TrainingObserver : public DecoderObserver {
-  TrainingObserver(const int k, const DocScorer& d, bool sf, vector<GoodBadOracle>* o) : ds(d), oracles(*o), kbest_size(k), sample_forest(sf) {}
-  const DocScorer& ds;
+  TrainingObserver(const int k, const DocumentScorer& d, const EvaluationMetric& m, bool sf, vector<GoodBadOracle>* o) : ds(d), metric(m), oracles(*o), kbest_size(k), sample_forest(sf) {}
+  const DocumentScorer& ds;
+  const EvaluationMetric& metric;
   vector<GoodBadOracle>& oracles;
   std::tr1::shared_ptr<HypothesisInfo> cur_best;
   const int kbest_size;
@@ -121,13 +124,16 @@ struct TrainingObserver : public DecoderObserver {
     if (sample_forest) {
       vector<WordID> cur_prediction;
       ViterbiESentence(forest, &cur_prediction);
-      float sentscore = ds[sent_id]->ScoreCandidate(cur_prediction)->ComputeScore();
+      SufficientStats sstats;
+      ds[sent_id]->Evaluate(cur_prediction, &sstats);
+      float sentscore = metric.ComputeScore(sstats);
       cur_best = MakeHypothesisInfo(ViterbiFeatures(forest), sentscore);
 
       vector<HypergraphSampler::Hypothesis> samples;
       HypergraphSampler::sample_hypotheses(forest, kbest_size, &*rng, &samples);
       for (unsigned i = 0; i < samples.size(); ++i) {
-        sentscore = ds[sent_id]->ScoreCandidate(samples[i].words)->ComputeScore();
+        ds[sent_id]->Evaluate(samples[i].words, &sstats);
+        float sentscore = metric.ComputeScore(sstats);
         if (invert_score) sentscore *= -1.0;
         if (!cur_good || sentscore > cur_good->mt_metric)
           cur_good = MakeHypothesisInfo(samples[i].fmap, sentscore);
@@ -136,11 +142,13 @@ struct TrainingObserver : public DecoderObserver {
       }
     } else {
       KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size);
+      SufficientStats sstats;
       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();
+        ds[sent_id]->Evaluate(d->yield, &sstats);
+        float sentscore = metric.ComputeScore(sstats);
         if (invert_score) sentscore *= -1.0;
         // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl;
         if (i == 0)
@@ -192,15 +200,20 @@ int main(int argc, char** argv) {
   }
   vector<string> corpus;
   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;
+
+  string metric_name = UppercaseString(conf["evaluation_metric"].as<string>());
+  if (metric_name == "COMBI") {
+    cerr << "WARNING: 'combi' metric is no longer supported, switching to 'COMB:TER=-0.5;IBM_BLEU=0.5'\n";
+    metric_name = "COMB:TER=-0.5;IBM_BLEU=0.5";
+  } else if (metric_name == "BLEU") {
+    cerr << "WARNING: 'BLEU' is ambiguous, assuming 'IBM_BLEU'\n";
+    metric_name = "IBM_BLEU";
   }
-  DocScorer ds(type, conf["reference"].as<vector<string> >(), "");
+  EvaluationMetric* metric = EvaluationMetric::Instance(metric_name);
+  DocumentScorer ds(metric, conf["reference"].as<vector<string> >());
   cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl;
+  invert_score = metric->IsErrorMetric();
+
   if (ds.size() != corpus.size()) {
     cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n";
     return 1;
@@ -221,7 +234,7 @@ int main(int argc, char** argv) {
   assert(corpus.size() > 0);
   vector<GoodBadOracle> oracles(corpus.size());
 
-  TrainingObserver observer(conf["k_best_size"].as<int>(), ds, sample_forest, &oracles);
+  TrainingObserver observer(conf["k_best_size"].as<int>(), ds, *metric, sample_forest, &oracles);
   int cur_sent = 0;
   int lcount = 0;
   int normalizer = 0;