Merge remote-tracking branch 'upstream/master'

3 files changed, 22 insertions, 177 deletions

diff --git a/pro-train/Makefile.am b/pro-train/Makefile.am
index 11d26211..1e9d46b0 100644
--- a/pro-train/Makefile.am
+++ b/pro-train/Makefile.am
@@ -2,12 +2,10 @@ bin_PROGRAMS = \
   mr_pro_map \
   mr_pro_reduce
 
-TESTS = lo_test
-
 mr_pro_map_SOURCES = mr_pro_map.cc
-mr_pro_map_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz
+mr_pro_map_LDADD = $(top_srcdir)/training/libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz
 
 mr_pro_reduce_SOURCES = mr_pro_reduce.cc
-mr_pro_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/training/optimize.o $(top_srcdir)/training/liblbfgs/liblbfgs.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz
+mr_pro_reduce_LDADD = $(top_srcdir)/training/liblbfgs/liblbfgs.a $(top_srcdir)/utils/libutils.a -lz
 
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I$(top_srcdir)/training
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I$(top_srcdir)/training
diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
index 52b67f32..eef40b8a 100644
--- a/pro-train/mr_pro_map.cc
+++ b/pro-train/mr_pro_map.cc
@@ -9,14 +9,13 @@
 #include <boost/program_options.hpp>
 #include <boost/program_options/variables_map.hpp>
 
+#include "candidate_set.h"
 #include "sampler.h"
 #include "filelib.h"
 #include "stringlib.h"
 #include "weights.h"
 #include "inside_outside.h"
 #include "hg_io.h"
-#include "kbest.h"
-#include "viterbi.h"
 #include "ns.h"
 #include "ns_docscorer.h"
 
@@ -25,52 +24,6 @@
 using namespace std;
 namespace po = boost::program_options;
 
-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<weight_t>& x) const {
-    size_t h = 0x573915839;
-    for (SparseVector<weight_t>::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<weight_t>& a, const SparseVector<weight_t>& b) const {
-    SparseVector<weight_t>::const_iterator bit = b.begin();
-    for (SparseVector<weight_t>::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;
-  }
-};
-
 boost::shared_ptr<MT19937> rng;
 
 void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
@@ -105,107 +58,6 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   }
 }
 
-struct HypInfo {
-  HypInfo() : g_(-100.0f) {}
-  HypInfo(const vector<WordID>& h, const SparseVector<weight_t>& feats) : hyp(h), g_(-100.0f), x(feats) {}
-
-  // lazy evaluation
-  double g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const {
-    if (g_ == -100.0f) {
-      SufficientStats ss;
-      scorer.Evaluate(hyp, &ss);
-      g_ = metric->ComputeScore(ss);
-    }
-    return g_;
-  }
-  vector<WordID> hyp;
-  mutable float g_;
-  SparseVector<weight_t> x;
-};
-
-struct HypInfoCompare {
-  bool operator()(const HypInfo& a, const HypInfo& b) const {
-    ApproxVectorEquals comp;
-    return (a.hyp == b.hyp && comp(a.x,b.x));
-  }
-};
-
-struct HypInfoHasher {
-  size_t operator()(const HypInfo& x) const {
-    boost::hash<vector<WordID> > hhasher;
-    ApproxVectorHasher vhasher;
-    size_t ha = hhasher(x.hyp);
-    boost::hash_combine(ha, vhasher(x.x));
-    return ha;
-  }
-};
-
-void WriteKBest(const string& file, const vector<HypInfo>& kbest) {
-  WriteFile wf(file);
-  ostream& out = *wf.stream();
-  out.precision(10);
-  for (int i = 0; i < kbest.size(); ++i) {
-    out << TD::GetString(kbest[i].hyp) << endl;
-    out << kbest[i].x << endl;
-  }
-}
-
-void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) {
-  SparseVector<weight_t>& 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 ReadKBest(const string& file, vector<HypInfo>* kbest) {
-  cerr << "Reading from " << file << endl;
-  ReadFile rf(file);
-  istream& in = *rf.stream();
-  string cand;
-  string feats;
-  while(getline(in, cand)) {
-    getline(in, feats);
-    assert(in);
-    kbest->push_back(HypInfo());
-    TD::ConvertSentence(cand, &kbest->back().hyp);
-    ParseSparseVector(feats, 0, &kbest->back().x);
-  }
-  cerr << "  read " << kbest->size() << " hypotheses\n";
-}
-
-void Dedup(vector<HypInfo>* h) {
-  cerr << "Dedup in=" << h->size();
-  tr1::unordered_set<HypInfo, HypInfoHasher, HypInfoCompare> u;
-  while(h->size() > 0) {
-    u.insert(h->back());
-    h->pop_back();
-  }
-  tr1::unordered_set<HypInfo, HypInfoHasher, HypInfoCompare>::iterator it = u.begin();
-  while (it != u.end()) {
-    h->push_back(*it);
-    it = u.erase(it);
-  }
-  cerr << "  out=" << h->size() << endl;
-}
-
 struct ThresholdAlpha {
   explicit ThresholdAlpha(double t = 0.05) : threshold(t) {}
   double operator()(double mag) const {
@@ -239,8 +91,7 @@ struct DiffOrder {
 
 void Sample(const unsigned gamma,
             const unsigned xi,
-            const vector<HypInfo>& J_i,
-            const SegmentEvaluator& scorer,
+            const training::CandidateSet& J_i,
             const EvaluationMetric* metric,
             vector<TrainingInstance>* pv) {
   const bool invert_score = metric->IsErrorMetric();
@@ -250,17 +101,17 @@ 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].eval_feats);
+    float gb = metric->ComputeScore(J_i[b].eval_feats);
     bool positive = gb < ga;
     if (invert_score) positive = !positive;
     const float gdiff = fabs(ga - gb);
     if (!gdiff) continue;
     avg_diff += gdiff;
-    SparseVector<weight_t> xdiff = (J_i[a].x - J_i[b].x).erase_zeros();
+    SparseVector<weight_t> xdiff = (J_i[a].fmap - J_i[b].fmap).erase_zeros();
     if (xdiff.empty()) {
-      cerr << "Empty diff:\n  " << TD::GetString(J_i[a].hyp) << endl << "x=" << J_i[a].x << endl;
-      cerr << "  " << TD::GetString(J_i[b].hyp) << endl << "x=" << J_i[b].x << endl;
+      cerr << "Empty diff:\n  " << TD::GetString(J_i[a].ewords) << endl << "x=" << J_i[a].fmap << endl;
+      cerr << "  " << TD::GetString(J_i[b].ewords) << endl << "x=" << J_i[b].fmap << endl;
       continue;
     }
     v1.push_back(TrainingInstance(xdiff, positive, gdiff));
@@ -328,25 +179,17 @@ int main(int argc, char** argv) {
     is >> file >> sent_id;
     ReadFile rf(file);
     ostringstream os;
-    vector<HypInfo> J_i;
+    training::CandidateSet J_i;
     os << kbest_repo << "/kbest." << sent_id << ".txt.gz";
     const string kbest_file = os.str();
     if (FileExists(kbest_file))
-      ReadKBest(kbest_file, &J_i);
+      J_i.ReadFromFile(kbest_file);
     HypergraphIO::ReadFromJSON(rf.stream(), &hg);
     hg.Reweight(weights);
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
-
-    for (int i = 0; i < kbest_size; ++i) {
-      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
-        kbest.LazyKthBest(hg.nodes_.size() - 1, i);
-      if (!d) break;
-      J_i.push_back(HypInfo(d->yield, d->feature_values));
-    }
-    Dedup(&J_i);
-    WriteKBest(kbest_file, J_i);
+    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/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
index d3fb8026..5ef9b470 100644
--- a/pro-train/mr_pro_reduce.cc
+++ b/pro-train/mr_pro_reduce.cc
@@ -25,6 +25,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   opts.add_options()
         ("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation")
         ("regularization_strength,C",po::value<double>()->default_value(500.0), "l2 regularization strength")
+        ("l1",po::value<double>()->default_value(0.0), "l1 regularization strength")
         ("regularize_to_weights,y",po::value<double>()->default_value(5000.0), "Differences in learned weights to previous weights are penalized with an l2 penalty with this strength; 0.0 = no effect")
         ("memory_buffers,m",po::value<unsigned>()->default_value(100), "Number of memory buffers (LBFGS)")
         ("min_reg,r",po::value<double>()->default_value(0.01), "When tuning (-T) regularization strength, minimum regularization strenght")
@@ -180,12 +181,14 @@ struct ProLoss {
 double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training,
                        const vector<pair<bool, SparseVector<weight_t> > >& testing,
                        const double C,
+                       const double C1,
                        const double T,
                        const unsigned memory_buffers,
                        const vector<weight_t>& prev_x,
                        vector<weight_t>* px) {
+  assert(px->size() == prev_x.size());
   ProLoss loss(training, testing, C, T, prev_x);
-  LBFGS<ProLoss> lbfgs(px, loss, 0.0, memory_buffers);
+  LBFGS<ProLoss> lbfgs(px, loss, memory_buffers, C1);
   lbfgs.MinimizeFunction();
   return loss.tppl;
 }
@@ -203,6 +206,7 @@ int main(int argc, char** argv) {
   const double min_reg = conf["min_reg"].as<double>();
   const double max_reg = conf["max_reg"].as<double>();
   double C = conf["regularization_strength"].as<double>(); // will be overridden if parameter is tuned
+  double C1 = conf["l1"].as<double>(); // will be overridden if parameter is tuned
   const double T = conf["regularize_to_weights"].as<double>();
   assert(C >= 0.0);
   assert(min_reg >= 0.0);
@@ -239,7 +243,7 @@ int main(int argc, char** argv) {
     cerr << "SWEEP FACTOR: " << sweep_factor << endl;
     while(C < max_reg) {
       cerr << "C=" << C << "\tT=" <<T << endl;
-      tppl = LearnParameters(training, testing, C, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x);
+      tppl = LearnParameters(training, testing, C, C1, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x);
       sp.push_back(make_pair(C, tppl));
       C *= sweep_factor;
     }
@@ -262,7 +266,7 @@ int main(int argc, char** argv) {
     }
     C = sp[best_i].first;
   }  // tune regularizer
-  tppl = LearnParameters(training, testing, C, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x);
+  tppl = LearnParameters(training, testing, C, C1, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x);
   if (conf.count("weights")) {
     for (int i = 1; i < x.size(); ++i) {
       x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi);