finalized merge

1 files changed, 347 insertions, 0 deletions

diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
new file mode 100644
index 00000000..0a9b75d7
--- /dev/null
+++ b/pro-train/mr_pro_map.cc
@@ -0,0 +1,347 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <tr1/unordered_map>
+
+#include <boost/functional/hash.hpp>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "sampler.h"
+#include "filelib.h"
+#include "stringlib.h"
+#include "weights.h"
+#include "scorer.h"
+#include "inside_outside.h"
+#include "hg_io.h"
+#include "kbest.h"
+#include "viterbi.h"
+
+// This is Figure 4 (Algorithm Sampler) from Hopkins&May (2011)
+
+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) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation (tokenized text)")
+        ("weights,w",po::value<string>(), "[REQD] Weights files from current iterations")
+        ("kbest_repository,K",po::value<string>()->default_value("./kbest"),"K-best list repository (directory)")
+        ("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
+        ("source,s",po::value<string>()->default_value(""), "Source file (ignored, except for AER)")
+        ("loss_function,l",po::value<string>()->default_value("ibm_bleu"), "Loss function being optimized")
+        ("kbest_size,k",po::value<unsigned>()->default_value(1500u), "Top k-hypotheses to extract")
+        ("candidate_pairs,G", po::value<unsigned>()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)")
+        ("best_pairs,X", po::value<unsigned>()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+        ("help,h", "Help");
+  po::options_description dcmdline_options;
+  dcmdline_options.add(opts);
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  bool flag = false;
+  if (!conf->count("reference")) {
+    cerr << "Please specify one or more references using -r <REF.TXT>\n";
+    flag = true;
+  }
+  if (!conf->count("weights")) {
+    cerr << "Please specify weights using -w <WEIGHTS.TXT>\n";
+    flag = true;
+  }
+  if (flag || conf->count("help")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+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 SentenceScorer& scorer) const {
+    if (g_ == -100.0f)
+      g_ = scorer.ScoreCandidate(hyp)->ComputeScore();
+    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 {
+    if (mag < threshold) return 0.0; else return 1.0;
+  }
+  const double threshold;
+};
+
+struct TrainingInstance {
+  TrainingInstance(const SparseVector<weight_t>& feats, bool positive, float diff) : x(feats), y(positive), gdiff(diff) {}
+  SparseVector<weight_t> x;
+#undef DEBUGGING_PRO
+#ifdef DEBUGGING_PRO
+  vector<WordID> a;
+  vector<WordID> b;
+#endif
+  bool y;
+  float gdiff;
+};
+#ifdef DEBUGGING_PRO
+ostream& operator<<(ostream& os, const TrainingInstance& d) {
+  return os << d.gdiff << " y=" << d.y << "\tA:" << TD::GetString(d.a) << "\n\tB: " << TD::GetString(d.b) << "\n\tX: " << d.x;
+}
+#endif
+
+struct DiffOrder {
+  bool operator()(const TrainingInstance& a, const TrainingInstance& b) const {
+    return a.gdiff > b.gdiff;
+  }
+};
+
+void Sample(const unsigned gamma, const unsigned xi, const vector<HypInfo>& J_i, const SentenceScorer& scorer, const bool invert_score, vector<TrainingInstance>* pv) {
+  vector<TrainingInstance> v1, v2;
+  float avg_diff = 0;
+  for (unsigned i = 0; i < gamma; ++i) {
+    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);
+    float gb = J_i[b].g(scorer);
+    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();
+    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;
+      continue;
+    }
+    v1.push_back(TrainingInstance(xdiff, positive, gdiff));
+#ifdef DEBUGGING_PRO
+    v1.back().a = J_i[a].hyp;
+    v1.back().b = J_i[b].hyp;
+    cerr << "N: " << v1.back() << endl;
+#endif
+  }
+  avg_diff /= v1.size();
+
+  for (unsigned i = 0; i < v1.size(); ++i) {
+    double p = 1.0 / (1.0 + exp(-avg_diff - v1[i].gdiff));
+    // cerr << "avg_diff=" << avg_diff << "  gdiff=" << v1[i].gdiff << "  p=" << p << endl;
+    if (rng->next() < p) v2.push_back(v1[i]);
+  }
+  vector<TrainingInstance>::iterator mid = v2.begin() + xi;
+  if (xi > v2.size()) mid = v2.end();
+  partial_sort(v2.begin(), mid, v2.end(), DiffOrder());
+  copy(v2.begin(), mid, back_inserter(*pv));
+#ifdef DEBUGGING_PRO
+  if (v2.size() >= 5) {
+    for (int i =0; i < (mid - v2.begin()); ++i) {
+      cerr << v2[i] << endl;
+    }
+    cerr << pv->back() << endl;
+  }
+#endif
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
+  const string loss_function = conf["loss_function"].as<string>();
+
+  ScoreType type = ScoreTypeFromString(loss_function);
+  DocScorer ds(type, conf["reference"].as<vector<string> >(), conf["source"].as<string>());
+  cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function << endl;
+  Hypergraph hg;
+  string last_file;
+  ReadFile in_read(conf["input"].as<string>());
+  istream &in=*in_read.stream();
+  const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
+  const unsigned gamma = conf["candidate_pairs"].as<unsigned>();
+  const unsigned xi = conf["best_pairs"].as<unsigned>();
+  string weightsf = conf["weights"].as<string>();
+  vector<weight_t> weights;
+  Weights::InitFromFile(weightsf, &weights);
+  string kbest_repo = conf["kbest_repository"].as<string>();
+  MkDirP(kbest_repo);
+  while(in) {
+    vector<TrainingInstance> v;
+    string line;
+    getline(in, line);
+    if (line.empty()) continue;
+    istringstream is(line);
+    int sent_id;
+    string file;
+    // path-to-file (JSON) sent_id
+    is >> file >> sent_id;
+    ReadFile rf(file);
+    ostringstream os;
+    vector<HypInfo> 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);
+    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);
+
+    Sample(gamma, xi, J_i, *ds[sent_id], (type == TER), &v);
+    for (unsigned i = 0; i < v.size(); ++i) {
+      const TrainingInstance& vi = v[i];
+      cout << vi.y << "\t" << vi.x << endl;
+      cout << (!vi.y) << "\t" << (vi.x * -1.0) << endl;
+    }
+  }
+  return 0;
+}
+