initial checkin

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@2 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 445 insertions, 0 deletions

diff --git a/decoder/ff_wordalign.cc b/decoder/ff_wordalign.cc
new file mode 100644
index 00000000..669aa530
--- /dev/null
+++ b/decoder/ff_wordalign.cc
@@ -0,0 +1,445 @@
+#include "ff_wordalign.h"
+
+#include <set>
+#include <sstream>
+#include <string>
+#include <cmath>
+
+#include "stringlib.h"
+#include "sentence_metadata.h"
+#include "hg.h"
+#include "fdict.h"
+#include "aligner.h"
+#include "tdict.h"   // Blunsom hack
+#include "filelib.h" // Blunsom hack
+
+static const int MAX_SENTENCE_SIZE = 100;
+
+using namespace std;
+
+Model2BinaryFeatures::Model2BinaryFeatures(const string& param) :
+    fids_(boost::extents[MAX_SENTENCE_SIZE][MAX_SENTENCE_SIZE][MAX_SENTENCE_SIZE]) {
+  for (int i = 1; i < MAX_SENTENCE_SIZE; ++i) {
+    for (int j = 0; j < i; ++j) {
+      for (int k = 0; k < MAX_SENTENCE_SIZE; ++k) {
+        int& val = fids_[i][j][k];
+        val = -1;
+        if (j < i) {
+          ostringstream os;
+          os << "M2FL:" << i << ":TI:" << k << "_SI:" << j;
+          val = FD::Convert(os.str());
+        }
+      }
+    }
+  }
+}
+
+void Model2BinaryFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                                 const Hypergraph::Edge& edge,
+                                                 const vector<const void*>& ant_states,
+                                                 SparseVector<double>* features,
+                                                 SparseVector<double>* estimated_features,
+                                                 void* state) const {
+  // if the source word is either null or the generated word
+  // has no position in the reference
+  if (edge.i_ == -1 || edge.prev_i_ == -1)
+    return;
+
+  assert(smeta.GetTargetLength() > 0);
+  const int fid = fids_[smeta.GetSourceLength()][edge.i_][edge.prev_i_];
+  features->set_value(fid, 1.0);
+//  cerr << f_len_ << " " << e_len_ << " [" << edge.i_ << "," << edge.j_ << "|" << edge.prev_i_ << "," << edge.prev_j_ << "]\t" << edge.rule_->AsString() << "\tVAL=" << val << endl;
+}
+
+
+RelativeSentencePosition::RelativeSentencePosition(const string& param) :
+    fid_(FD::Convert("RelativeSentencePosition")) {
+  if (!param.empty()) {
+    cerr << "  Loading word classes from " << param << endl;
+    condition_on_fclass_ = true;
+    ReadFile rf(param);
+    istream& in = *rf.stream();
+    set<WordID> classes;
+    while(in) {
+      string line;
+      getline(in, line);
+      if (line.empty()) continue;
+      vector<WordID> v;
+      TD::ConvertSentence(line, &v);
+      pos_.push_back(v);
+      for (int i = 0; i < v.size(); ++i)
+        classes.insert(v[i]);
+      for (set<WordID>::iterator i = classes.begin(); i != classes.end(); ++i) {
+        ostringstream os;
+        os << "RelPos_FC:" << TD::Convert(*i);
+        fids_[*i] = FD::Convert(os.str());
+      }
+    }
+  } else {
+    condition_on_fclass_ = false;
+  }
+}
+
+void RelativeSentencePosition::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                                     const Hypergraph::Edge& edge,
+                                                     const vector<const void*>& ant_states,
+                                                     SparseVector<double>* features,
+                                                     SparseVector<double>* estimated_features,
+                                                     void* state) const {
+  // if the source word is either null or the generated word
+  // has no position in the reference
+  if (edge.i_ == -1 || edge.prev_i_ == -1)
+    return;
+
+  assert(smeta.GetTargetLength() > 0);
+  const double val = fabs(static_cast<double>(edge.i_) / smeta.GetSourceLength() -
+                          static_cast<double>(edge.prev_i_) / smeta.GetTargetLength());
+  features->set_value(fid_, val);
+  if (condition_on_fclass_) {
+    assert(smeta.GetSentenceID() < pos_.size());
+    const WordID cur_fclass = pos_[smeta.GetSentenceID()][edge.i_];
+    const int fid = fids_.find(cur_fclass)->second;
+    features->set_value(fid, val);
+  }
+//  cerr << f_len_ << " " << e_len_ << " [" << edge.i_ << "," << edge.j_ << "|" << edge.prev_i_ << "," << edge.prev_j_ << "]\t" << edge.rule_->AsString() << "\tVAL=" << val << endl;
+}
+
+MarkovJumpFClass::MarkovJumpFClass(const string& param) :
+    FeatureFunction(1),
+    fids_(MAX_SENTENCE_SIZE) {
+  cerr << "    MarkovJumpFClass" << endl;
+  cerr << "Reading source POS tags from " << param << endl;
+  ReadFile rf(param);
+  istream& in = *rf.stream();
+  set<WordID> classes;
+  while(in) {
+    string line;
+    getline(in, line);
+    if (line.empty()) continue;
+    vector<WordID> v;
+    TD::ConvertSentence(line, &v);
+    pos_.push_back(v);
+    for (int i = 0; i < v.size(); ++i)
+      classes.insert(v[i]);
+  }
+  cerr << "  (" << pos_.size() << " lines)\n";
+  cerr << "  Classes: " << classes.size() << endl;
+  for (int ss = 1; ss < MAX_SENTENCE_SIZE; ++ss) {
+    map<WordID, map<int, int> >& cfids = fids_[ss];
+    for (set<WordID>::iterator i = classes.begin(); i != classes.end(); ++i) {
+      map<int, int> &fids = cfids[*i];
+      for (int j = -ss; j <= ss; ++j) {
+        ostringstream os;
+        os << "Jump_FL:" << ss << "_FC:" << TD::Convert(*i) << "_J:" << j;
+        fids[j] = FD::Convert(os.str());
+      }
+    }
+  }
+}
+
+void MarkovJumpFClass::FireFeature(const SentenceMetadata& smeta,
+                                   int prev_src_pos,
+                                   int cur_src_pos,
+                                   SparseVector<double>* features) const {
+  const int jumpsize = cur_src_pos - prev_src_pos;
+  assert(smeta.GetSentenceID() < pos_.size());
+  const WordID cur_fclass = pos_[smeta.GetSentenceID()][cur_src_pos];
+  const int fid = fids_[smeta.GetSourceLength()].find(cur_fclass)->second.find(jumpsize)->second;
+  features->set_value(fid, 1.0);
+}
+
+void MarkovJumpFClass::FinalTraversalFeatures(const void* context,
+                                      SparseVector<double>* features) const {
+  int left_index = *static_cast<const unsigned char*>(context);
+//  int right_index = cur_flen;
+  // TODO
+}
+
+void MarkovJumpFClass::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const std::vector<const void*>& ant_states,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* state) const {
+  unsigned char& dpstate = *((unsigned char*)state);
+  if (edge.Arity() == 0) {
+    dpstate = static_cast<unsigned int>(edge.i_);
+  } else if (edge.Arity() == 1) {
+    dpstate = *((unsigned char*)ant_states[0]);
+  } else if (edge.Arity() == 2) {
+    int left_index = *((unsigned char*)ant_states[0]);
+    int right_index = *((unsigned char*)ant_states[1]);
+    if (right_index == -1)
+      dpstate = static_cast<unsigned int>(left_index);
+    else
+      dpstate = static_cast<unsigned int>(right_index);
+//    const WordID cur_fclass = pos_[smeta.GetSentenceID()][right_index];
+//    cerr << edge.i_ << "," << edge.j_ << ": fclass=" << TD::Convert(cur_fclass) << " j=" << jumpsize << endl;
+//    const int fid = fids_[smeta.GetSourceLength()].find(cur_fclass)->second.find(jumpsize)->second;
+//    features->set_value(fid, 1.0);
+    FireFeature(smeta, left_index, right_index, features);
+  }
+}
+
+//  std::vector<std::map<int, int> > flen2jump2fid_;
+MarkovJump::MarkovJump(const string& param) :
+    FeatureFunction(1),
+    fid_(FD::Convert("MarkovJump")),
+    binary_params_(false) {
+  cerr << "    MarkovJump";
+  vector<string> argv;
+  int argc = SplitOnWhitespace(param, &argv);
+  if (argc != 1 || !(argv[0] == "-b" || argv[0] == "+b")) {
+    cerr << "MarkovJump: expected parameters to be -b or +b\n";
+    exit(1);
+  }
+  binary_params_ = argv[0] == "+b";
+  if (binary_params_) {
+    flen2jump2fid_.resize(MAX_SENTENCE_SIZE);
+    for (int i = 1; i < MAX_SENTENCE_SIZE; ++i) {
+      map<int, int>& jump2fid = flen2jump2fid_[i];
+      for (int jump = -i; jump <= i; ++jump) {
+        ostringstream os;
+        os << "Jump:FLen:" << i << "_J:" << jump;
+        jump2fid[jump] = FD::Convert(os.str());
+      }
+    }
+  } else {
+    cerr << " (Blunsom & Cohn definition)";
+  }
+  cerr << endl;
+}
+
+// TODO handle NULLs according to Och 2000
+void MarkovJump::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                       const Hypergraph::Edge& edge,
+                                       const vector<const void*>& ant_states,
+                                       SparseVector<double>* features,
+                                       SparseVector<double>* estimated_features,
+                                       void* state) const {
+  unsigned char& dpstate = *((unsigned char*)state);
+  const int flen = smeta.GetSourceLength();
+  if (edge.Arity() == 0) {
+    dpstate = static_cast<unsigned int>(edge.i_);
+    if (edge.prev_i_ == 0) {
+      if (binary_params_) {
+        // NULL will be tricky
+        // TODO initial state distribution, not normal jumps
+        const int fid = flen2jump2fid_[flen].find(edge.i_ + 1)->second;
+        features->set_value(fid, 1.0);
+      }
+    } else if (edge.prev_i_ == smeta.GetTargetLength() - 1) {
+        // NULL will be tricky
+      if (binary_params_) {
+        int jumpsize = flen - edge.i_;
+        const int fid = flen2jump2fid_[flen].find(jumpsize)->second;
+        features->set_value(fid, 1.0);
+      }
+    }
+  } else if (edge.Arity() == 1) {
+    dpstate = *((unsigned char*)ant_states[0]);
+  } else if (edge.Arity() == 2) {
+    int left_index = *((unsigned char*)ant_states[0]);
+    int right_index = *((unsigned char*)ant_states[1]);
+    if (right_index == -1)
+      dpstate = static_cast<unsigned int>(left_index);
+    else
+      dpstate = static_cast<unsigned int>(right_index);
+    const int jumpsize = right_index - left_index;
+
+    if (binary_params_) {
+      const int fid = flen2jump2fid_[flen].find(jumpsize)->second;
+      features->set_value(fid, 1.0);
+    } else {
+      features->set_value(fid_, fabs(jumpsize - 1));  // Blunsom and Cohn def
+    }
+  } else {
+    assert(!"something really unexpected is happening");
+  }
+}
+
+// state: POS of src word used, number of trg words generated
+SourcePOSBigram::SourcePOSBigram(const std::string& param) :
+    FeatureFunction(sizeof(WordID) + sizeof(int)) {
+  cerr << "Reading source POS tags from " << param << endl;
+  ReadFile rf(param);
+  istream& in = *rf.stream();
+  while(in) {
+    string line;
+    getline(in, line);
+    if (line.empty()) continue;
+    vector<WordID> v;
+    TD::ConvertSentence(line, &v);
+    pos_.push_back(v);
+  }
+  cerr << "  (" << pos_.size() << " lines)\n";
+}
+
+void SourcePOSBigram::FinalTraversalFeatures(const void* context,
+                                      SparseVector<double>* features) const {
+  WordID left = *static_cast<const WordID*>(context);
+  int left_wc = *(static_cast<const int*>(context) + 1);
+  if (left_wc == 1)
+    FireFeature(-1, left, features);
+  FireFeature(left, -1, features);
+}
+
+void SourcePOSBigram::FireFeature(WordID left,
+                   WordID right,
+                   SparseVector<double>* features) const {
+  int& fid = fmap_[left][right];
+  if (!fid) {
+    ostringstream os;
+    os << "SP:";
+    if (left < 0) { os << "BOS"; } else { os << TD::Convert(left); }
+    os << '_';
+    if (right < 0) { os << "EOS"; } else { os << TD::Convert(right); }
+    fid = FD::Convert(os.str());
+    if (fid == 0) fid = -1;
+  }
+  if (fid < 0) return;
+  features->set_value(fid, 1.0);
+}
+
+void SourcePOSBigram::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const std::vector<const void*>& ant_contexts,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* context) const {
+  WordID& out_context = *static_cast<WordID*>(context);
+  int& out_word_count = *(static_cast<int*>(context) + 1);
+  const int arity = edge.Arity();
+  if (arity == 0) {
+    assert(smeta.GetSentenceID() < pos_.size());
+    const vector<WordID>& pos_sent = pos_[smeta.GetSentenceID()];
+    assert(edge.i_ < pos_sent.size());
+    out_context = pos_sent[edge.i_];
+    out_word_count = edge.rule_->EWords();
+    assert(out_word_count == 1); // this is only defined for lex translation!
+    // revisit this if you want to translate into null words
+  } else if (arity == 2) {
+    WordID left = *static_cast<const WordID*>(ant_contexts[0]);
+    WordID right = *static_cast<const WordID*>(ant_contexts[1]);
+    int left_wc = *(static_cast<const int*>(ant_contexts[0]) + 1);
+    int right_wc = *(static_cast<const int*>(ant_contexts[0]) + 1);
+    if (left_wc == 1 && right_wc == 1)
+      FireFeature(-1, left, features);
+    FireFeature(left, right, features);
+    out_word_count = left_wc + right_wc;
+    out_context = right;
+  }
+}
+
+AlignerResults::AlignerResults(const std::string& param) :
+    cur_sent_(-1),
+    cur_grid_(NULL) {
+  vector<string> argv;
+  int argc = SplitOnWhitespace(param, &argv);
+  if (argc != 2) {
+    cerr << "Required format: AlignerResults [FeatureName] [file.pharaoh]\n";
+    exit(1);
+  }
+  cerr << "  feature: " << argv[0] << "\talignments: " << argv[1] << endl;
+  fid_ = FD::Convert(argv[0]);
+  ReadFile rf(argv[1]);
+  istream& in = *rf.stream(); int lc = 0;
+  while(in) {
+    string line;
+    getline(in, line);
+    if (!in) break; 
+    ++lc;
+    is_aligned_.push_back(AlignerTools::ReadPharaohAlignmentGrid(line));
+  }
+  cerr << "  Loaded " << lc << " refs\n";
+}
+
+void AlignerResults::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                           const Hypergraph::Edge& edge,
+                                           const vector<const void*>& ant_states,
+                                           SparseVector<double>* features,
+                                           SparseVector<double>* estimated_features,
+                                           void* state) const {
+  if (edge.i_ == -1 || edge.prev_i_ == -1)
+    return;
+
+  if (cur_sent_ != smeta.GetSentenceID()) {
+    assert(smeta.HasReference());
+    cur_sent_ = smeta.GetSentenceID();
+    assert(cur_sent_ < is_aligned_.size());
+    cur_grid_ = is_aligned_[cur_sent_].get();
+  }
+
+  //cerr << edge.rule_->AsString() << endl;
+
+  int j = edge.i_;        // source side (f)
+  int i = edge.prev_i_;   // target side (e)
+  if (j < cur_grid_->height() && i < cur_grid_->width() && (*cur_grid_)(i, j)) {
+//    if (edge.rule_->e_[0] == smeta.GetReference()[i][0].label) {
+      features->set_value(fid_, 1.0);
+//      cerr << edge.rule_->AsString() << "   (" << i << "," << j << ")\n";
+//    }
+  }
+}
+
+BlunsomSynchronousParseHack::BlunsomSynchronousParseHack(const string& param) :
+  FeatureFunction((100 / 8) + 1), fid_(FD::Convert("NotRef")), cur_sent_(-1) {
+  ReadFile rf(param);
+  istream& in = *rf.stream(); int lc = 0;
+  while(in) {
+    string line;
+    getline(in, line);
+    if (!in) break; 
+    ++lc;
+    refs_.push_back(vector<WordID>());
+    TD::ConvertSentence(line, &refs_.back());
+  }
+  cerr << "  Loaded " << lc << " refs\n";
+}
+
+void BlunsomSynchronousParseHack::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                           const Hypergraph::Edge& edge,
+                                           const vector<const void*>& ant_states,
+                                           SparseVector<double>* features,
+                                           SparseVector<double>* estimated_features,
+                                           void* state) const {
+  if (cur_sent_ != smeta.GetSentenceID()) {
+    // assert(smeta.HasReference());
+    cur_sent_ = smeta.GetSentenceID();
+    assert(cur_sent_ < refs_.size());
+    cur_ref_ = &refs_[cur_sent_];
+    cur_map_.clear();
+    for (int i = 0; i < cur_ref_->size(); ++i) {
+      vector<WordID> phrase;
+      for (int j = i; j < cur_ref_->size(); ++j) {
+        phrase.push_back((*cur_ref_)[j]);
+        cur_map_[phrase] = i;
+      }
+    }
+  }
+  //cerr << edge.rule_->AsString() << endl;
+  for (int i = 0; i < ant_states.size(); ++i) {
+    if (DoesNotBelong(ant_states[i])) {
+      //cerr << "  ant " << i << " does not belong\n";
+      return;
+    }
+  }
+  vector<vector<WordID> > ants(ant_states.size());
+  vector<const vector<WordID>* > pants(ant_states.size());
+  for (int i = 0; i < ant_states.size(); ++i) {
+    AppendAntecedentString(ant_states[i], &ants[i]);
+    //cerr << "  ant[" << i << "]: " << ((int)*(static_cast<const unsigned char*>(ant_states[i]))) << " " << TD::GetString(ants[i]) << endl;
+    pants[i] = &ants[i];
+  }
+  vector<WordID> yield;
+  edge.rule_->ESubstitute(pants, &yield);
+  //cerr << "YIELD: " << TD::GetString(yield) << endl;
+  Vec2Int::iterator it = cur_map_.find(yield);
+  if (it == cur_map_.end()) {
+    features->set_value(fid_, 1);
+    //cerr << "  BAD!\n";
+    return;
+  }
+  SetStateMask(it->second, it->second + yield.size(), state);
+}
+