fix mapper to use common candidate set code

5 files changed, 240 insertions, 68 deletions

diff --git a/training/Makefile.am b/training/Makefile.am
index 991ac210..8124b107 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -23,11 +23,17 @@ noinst_PROGRAMS = \
 
 TESTS = lbfgs_test optimize_test
 
-mpi_online_optimize_SOURCES = mpi_online_optimize.cc online_optimizer.cc
-mpi_online_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
+noinst_LIBRARIES = libtraining.a
+libtraining_a_SOURCES = \
+  candidate_set.cc \
+  optimize.cc \
+  online_optimizer.cc
 
-mpi_flex_optimize_SOURCES = mpi_flex_optimize.cc online_optimizer.cc optimize.cc
-mpi_flex_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
+mpi_online_optimize_SOURCES = mpi_online_optimize.cc
+mpi_online_optimize_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
+
+mpi_flex_optimize_SOURCES = mpi_flex_optimize.cc
+mpi_flex_optimize_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
 mpi_extract_reachable_SOURCES = mpi_extract_reachable.cc
 mpi_extract_reachable_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
@@ -35,8 +41,8 @@ mpi_extract_reachable_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mtev
 mpi_extract_features_SOURCES = mpi_extract_features.cc
 mpi_extract_features_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
-mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc optimize.cc
-mpi_batch_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
+mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc
+mpi_batch_optimize_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
 mpi_compute_cllh_SOURCES = mpi_compute_cllh.cc
 mpi_compute_cllh_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
@@ -50,14 +56,14 @@ test_ngram_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteva
 model1_SOURCES = model1.cc ttables.cc
 model1_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
 
-lbl_model_SOURCES = lbl_model.cc optimize.cc
-lbl_model_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
+lbl_model_SOURCES = lbl_model.cc
+lbl_model_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
 
 grammar_convert_SOURCES = grammar_convert.cc
 grammar_convert_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
 
-optimize_test_SOURCES = optimize_test.cc optimize.cc online_optimizer.cc
-optimize_test_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
+optimize_test_SOURCES = optimize_test.cc
+optimize_test_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
 
 collapse_weights_SOURCES = collapse_weights.cc
 collapse_weights_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
@@ -65,8 +71,8 @@ collapse_weights_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/lib
 lbfgs_test_SOURCES = lbfgs_test.cc
 lbfgs_test_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
 
-mr_optimize_reduce_SOURCES = mr_optimize_reduce.cc optimize.cc
-mr_optimize_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
+mr_optimize_reduce_SOURCES = mr_optimize_reduce.cc
+mr_optimize_reduce_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
 
 mr_em_map_adapter_SOURCES = mr_em_map_adapter.cc
 mr_em_map_adapter_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
diff --git a/training/candidate_set.cc b/training/candidate_set.cc
new file mode 100644
index 00000000..5ab4558a
--- /dev/null
+++ b/training/candidate_set.cc
@@ -0,0 +1,169 @@
+#include "candidate_set.h"
+
+#include <tr1/unordered_set>
+
+#include <boost/functional/hash.hpp>
+
+#include "ns.h"
+#include "filelib.h"
+#include "wordid.h"
+#include "tdict.h"
+#include "hg.h"
+#include "kbest.h"
+#include "viterbi.h"
+
+using namespace std;
+
+namespace training {
+
+struct ApproxVectorHasher {
+  static const size_t MASK = 0xFFFFFFFFull;
+  union UType {
+    double f;   // leave as double
+    size_t i;
+  };
+  static inline double round(const double x) {
+    UType t;
+    t.f = x;
+    size_t r = t.i & MASK;
+    if ((r << 1) > MASK)
+      t.i += MASK - r + 1;
+    else
+      t.i &= (1ull - MASK);
+    return t.f;
+  }
+  size_t operator()(const SparseVector<double>& x) const {
+    size_t h = 0x573915839;
+    for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) {
+      UType t;
+      t.f = it->second;
+      if (t.f) {
+        size_t z = (t.i >> 32);
+        boost::hash_combine(h, it->first);
+        boost::hash_combine(h, z);
+      }
+    }
+    return h;
+  }
+};
+
+struct ApproxVectorEquals {
+  bool operator()(const SparseVector<double>& a, const SparseVector<double>& b) const {
+    SparseVector<double>::const_iterator bit = b.begin();
+    for (SparseVector<double>::const_iterator ait = a.begin(); ait != a.end(); ++ait) {
+      if (bit == b.end() ||
+          ait->first != bit->first ||
+          ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second))
+        return false;
+      ++bit;
+    }
+    if (bit != b.end()) return false;
+    return true;
+  }
+};
+
+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;
+    return (a.ewords == b.ewords && eq(a.fmap,b.fmap));
+  }
+};
+
+struct CandidateHasher {
+  size_t operator()(const Candidate& x) const {
+    boost::hash<vector<WordID> > hhasher;
+    ApproxVectorHasher vhasher;
+    size_t ha = hhasher(x.ewords);
+    boost::hash_combine(ha, vhasher(x.fmap));
+    return ha;
+  }
+};
+
+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;
+  size_t last_comma = string::npos;
+  while(cur <= line.size()) {
+    if (line[cur] == ' ' || cur == line.size()) {
+      if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
+        cerr << "[ERROR] " << line << endl << "  position = " << cur << endl;
+        exit(1);
+      }
+      const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
+      if (cur < line.size()) line[cur] = 0;
+      const double val = strtod(&line[last_comma + 1], NULL);
+      x.set_value(fid, val);
+
+      last_comma = string::npos;
+      last_start = cur+1;
+    } else {
+      if (line[cur] == '=')
+        last_comma = cur;
+    }
+    ++cur;
+  }
+}
+
+void CandidateSet::ReadFromFile(const string& file) {
+  cerr << "Reading candidates from " << file << endl;
+  ReadFile rf(file);
+  istream& in = *rf.stream();
+  string cand;
+  string feats;
+  while(getline(in, cand)) {
+    getline(in, feats);
+    assert(in);
+    cs.push_back(Candidate());
+    TD::ConvertSentence(cand, &cs.back().ewords);
+    ParseSparseVector(feats, 0, &cs.back().fmap);
+  }
+  cerr << "  read " << cs.size() << " candidates\n";
+}
+
+void CandidateSet::Dedup() {
+  cerr << "Dedup in=" << cs.size();
+  tr1::unordered_set<Candidate, CandidateHasher, CandidateCompare> u;
+  while(cs.size() > 0) {
+    u.insert(cs.back());
+    cs.pop_back();
+  }
+  tr1::unordered_set<Candidate, CandidateHasher, CandidateCompare>::iterator it = u.begin();
+  while (it != u.end()) {
+    cs.push_back(*it);
+    it = u.erase(it);
+  }
+  cerr << "  out=" << cs.size() << endl;
+}
+
+void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size) {
+  KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
+
+  for (unsigned i = 0; i < kbest_size; ++i) {
+    const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+      kbest.LazyKthBest(hg.nodes_.size() - 1, i);
+    if (!d) break;
+    cs.push_back(Candidate(d->yield, d->feature_values));
+  }
+  Dedup();
+}
+
+}
diff --git a/training/candidate_set.h b/training/candidate_set.h
new file mode 100644
index 00000000..e2b0b1ba
--- /dev/null
+++ b/training/candidate_set.h
@@ -0,0 +1,53 @@
+#ifndef _CANDIDATE_SET_H_
+#define _CANDIDATE_SET_H_
+
+#include <vector>
+#include <algorithm>
+
+#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) {}
+  std::vector<WordID> ewords;
+  SparseVector<double> fmap;
+  double g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const;
+  void swap(Candidate& other) {
+    std::swap(g_, other.g_);
+    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.
+// aggregated k-best lists, sample lists, etc.
+class CandidateSet {
+ public:
+  CandidateSet() {}
+  inline size_t size() const { return cs.size(); }
+  const Candidate& operator[](size_t i) const { return cs[i]; }
+
+  void ReadFromFile(const std::string& file);
+  void WriteToFile(const std::string& file) const;
+  void AddKBestCandidates(const Hypergraph& hg, size_t kbest_size);
+  // TODO add code to do unique k-best
+  // TODO add code to draw k samples
+
+ private:
+  void Dedup();
+  std::vector<Candidate> cs;
+};
+
+}
+
+#endif
diff --git a/training/kbest_repository.cc b/training/kbest_repository.cc
deleted file mode 100644
index 145b40a2..00000000
--- a/training/kbest_repository.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-#include "kbest_repository.h"
-
-#include <boost/functional/hash.hpp>
-
-using namespace std;
-
-struct ApproxVectorHasher {
-  static const size_t MASK = 0xFFFFFFFFull;
-  union UType {
-    double f;   // leave as double
-    size_t i;
-  };
-  static inline double round(const double x) {
-    UType t;
-    t.f = x;
-    size_t r = t.i & MASK;
-    if ((r << 1) > MASK)
-      t.i += MASK - r + 1;
-    else
-      t.i &= (1ull - MASK);
-    return t.f;
-  }
-  size_t operator()(const SparseVector<double>& x) const {
-    size_t h = 0x573915839;
-    for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) {
-      UType t;
-      t.f = it->second;
-      if (t.f) {
-        size_t z = (t.i >> 32);
-        boost::hash_combine(h, it->first);
-        boost::hash_combine(h, z);
-      }
-    }
-    return h;
-  }
-};
-
diff --git a/training/kbest_repository.h b/training/kbest_repository.h
deleted file mode 100644
index 0345394a..00000000
--- a/training/kbest_repository.h
+++ /dev/null
@@ -1,19 +0,0 @@
-#ifndef _KBEST_REPOSITORY_H_
-#define _KBEST_REPOSITORY_H_
-
-#include <vector>
-#include "wordid.h"
-#include "ns.h"
-#include "sparse_vector.h"
-
-class KBestRepository {
-  struct HypInfo {
-    std::vector<WordID> words;
-    SparseVector<double> x;
-    SufficientStats score_stats;
-  };
-
-  std::vector<HypInfo> candidates;
-};
-
-#endif