cache metric computation in pro

3 files changed, 31 insertions, 35 deletions

diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
index 2aa0dc6f..bb13fdf4 100644
--- a/pro-train/mr_pro_map.cc
+++ b/pro-train/mr_pro_map.cc
@@ -92,7 +92,6 @@ struct DiffOrder {
 void Sample(const unsigned gamma,
             const unsigned xi,
             const training::CandidateSet& J_i,
-            const SegmentEvaluator& scorer,
             const EvaluationMetric* metric,
             vector<TrainingInstance>* pv) {
   const bool invert_score = metric->IsErrorMetric();
@@ -102,8 +101,8 @@ void Sample(const unsigned gamma,
     const size_t a = rng->inclusive(0, J_i.size() - 1)();
     const size_t b = rng->inclusive(0, J_i.size() - 1)();
     if (a == b) continue;
-    float ga = J_i[a].g(scorer, metric);
-    float gb = J_i[b].g(scorer, metric);
+    float ga = metric->ComputeScore(J_i[a].score_stats);
+    float gb = metric->ComputeScore(J_i[b].score_stats);
     bool positive = gb < ga;
     if (invert_score) positive = !positive;
     const float gdiff = fabs(ga - gb);
@@ -187,10 +186,10 @@ int main(int argc, char** argv) {
       J_i.ReadFromFile(kbest_file);
     HypergraphIO::ReadFromJSON(rf.stream(), &hg);
     hg.Reweight(weights);
-    J_i.AddKBestCandidates(hg, kbest_size);
+    J_i.AddKBestCandidates(hg, kbest_size, ds[sent_id]);
     J_i.WriteToFile(kbest_file);
 
-    Sample(gamma, xi, J_i, *ds[sent_id], metric, &v);
+    Sample(gamma, xi, J_i, metric, &v);
     for (unsigned i = 0; i < v.size(); ++i) {
       const TrainingInstance& vi = v[i];
       cout << vi.y << "\t" << vi.x << endl;
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();
 }
diff --git a/training/candidate_set.h b/training/candidate_set.h
index e2b0b1ba..824a4de2 100644
--- a/training/candidate_set.h
+++ b/training/candidate_set.h
@@ -4,29 +4,27 @@
 #include <vector>
 #include <algorithm>
 
+#include "ns.h"
 #include "wordid.h"
 #include "sparse_vector.h"
 
 class Hypergraph;
-struct SegmentEvaluator;
-struct EvaluationMetric;
 
 namespace training {
 
 struct Candidate {
-  Candidate() : g_(-100.0f) {}
-  Candidate(const std::vector<WordID>& e, const SparseVector<double>& fm) : ewords(e), fmap(fm), g_(-100.0f) {}
+  Candidate() {}
+  Candidate(const std::vector<WordID>& e, const SparseVector<double>& fm) :
+    ewords(e),
+    fmap(fm) {}
   std::vector<WordID> ewords;
   SparseVector<double> fmap;
-  double g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const;
+  SufficientStats score_stats;
   void swap(Candidate& other) {
-    std::swap(g_, other.g_);
+    score_stats.swap(other.score_stats);
     ewords.swap(other.ewords);
     fmap.swap(other.fmap);
   }
- private:
-  mutable float g_;
-  //SufficientStats score_stats;
 };
 
 // represents some kind of collection of translation candidates, e.g.
@@ -39,7 +37,7 @@ class CandidateSet {
 
   void ReadFromFile(const std::string& file);
   void WriteToFile(const std::string& file) const;
-  void AddKBestCandidates(const Hypergraph& hg, size_t kbest_size);
+  void AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer = NULL);
   // TODO add code to do unique k-best
   // TODO add code to draw k samples