cache metric computation in pro

1 files changed, 19 insertions, 20 deletions

diff --git a/training/candidate_set.cc b/training/candidate_set.cc
index 5ab4558a..e2ca9ad2 100644
--- a/training/candidate_set.cc
+++ b/training/candidate_set.cc
@@ -62,15 +62,6 @@ struct ApproxVectorEquals {
   }
 };
 
-double Candidate::g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const {
-  if (g_ == -100.0f) {
-    SufficientStats ss;
-    scorer.Evaluate(ewords, &ss);
-    g_ = metric->ComputeScore(ss);
-  }
-  return g_;
-}
-
 struct CandidateCompare {
   bool operator()(const Candidate& a, const Candidate& b) const {
     ApproxVectorEquals eq;
@@ -88,16 +79,6 @@ struct CandidateHasher {
   }
 };
 
-void CandidateSet::WriteToFile(const string& file) const {
-  WriteFile wf(file);
-  ostream& out = *wf.stream();
-  out.precision(10);
-  for (unsigned i = 0; i < cs.size(); ++i) {
-    out << TD::GetString(cs[i].ewords) << endl;
-    out << cs[i].fmap << endl;
-  }
-}
-
 static void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
   SparseVector<double>& x = *out;
   size_t last_start = cur;
@@ -123,18 +104,34 @@ static void ParseSparseVector(string& line, size_t cur, SparseVector<double>* ou
   }
 }
 
+void CandidateSet::WriteToFile(const string& file) const {
+  WriteFile wf(file);
+  ostream& out = *wf.stream();
+  out.precision(10);
+  string ss;
+  for (unsigned i = 0; i < cs.size(); ++i) {
+    out << TD::GetString(cs[i].ewords) << endl;
+    out << cs[i].fmap << endl;
+    cs[i].score_stats.Encode(&ss);
+    out << ss << endl;
+  }
+}
+
 void CandidateSet::ReadFromFile(const string& file) {
   cerr << "Reading candidates from " << file << endl;
   ReadFile rf(file);
   istream& in = *rf.stream();
   string cand;
   string feats;
+  string ss;
   while(getline(in, cand)) {
     getline(in, feats);
+    getline(in, ss);
     assert(in);
     cs.push_back(Candidate());
     TD::ConvertSentence(cand, &cs.back().ewords);
     ParseSparseVector(feats, 0, &cs.back().fmap);
+    cs.back().score_stats = SufficientStats(ss);
   }
   cerr << "  read " << cs.size() << " candidates\n";
 }
@@ -154,7 +151,7 @@ void CandidateSet::Dedup() {
   cerr << "  out=" << cs.size() << endl;
 }
 
-void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size) {
+void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer) {
   KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
 
   for (unsigned i = 0; i < kbest_size; ++i) {
@@ -162,6 +159,8 @@ void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size) {
       kbest.LazyKthBest(hg.nodes_.size() - 1, i);
     if (!d) break;
     cs.push_back(Candidate(d->yield, d->feature_values));
+    if (scorer)
+      scorer->Evaluate(d->yield, &cs.back().score_stats);
   }
   Dedup();
 }