Merge branch 'master' of git://github.com/redpony/cdec

1 files changed, 169 insertions, 0 deletions

diff --git a/training/utils/candidate_set.cc b/training/utils/candidate_set.cc
new file mode 100644
index 00000000..087efec3
--- /dev/null
+++ b/training/utils/candidate_set.cc
@@ -0,0 +1,169 @@
+#include "candidate_set.h"
+
+#include <tr1/unordered_set>
+
+#include <boost/functional/hash.hpp>
+
+#include "verbose.h"
+#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;
+  }
+};
+
+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;
+  }
+};
+
+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::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].eval_feats.Encode(&ss);
+    out << ss << endl;
+  }
+}
+
+void CandidateSet::ReadFromFile(const string& file) {
+  if(!SILENT) 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().eval_feats = SufficientStats(ss);
+  }
+  if(!SILENT) cerr << "  read " << cs.size() << " candidates\n";
+}
+
+void CandidateSet::Dedup() {
+  if(!SILENT) 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);
+  }
+  if(!SILENT) cerr << "  out=" << cs.size() << endl;
+}
+
+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) {
+    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));
+    if (scorer)
+      scorer->Evaluate(d->yield, &cs.back().eval_feats);
+  }
+  Dedup();
+}
+
+}