crf training of arc-factored dep parser

1 files changed, 199 insertions, 1 deletions

diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index 7b5af4c1..def23edb 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -1,12 +1,210 @@
 #include "arc_factored.h"
 
+#include <vector>
 #include <iostream>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "arc_ff.h"
+#include "arc_ff_factory.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "tdict.h"
+#include "picojson.h"
+#include "optimize.h"
+#include "weights.h"
 
 using namespace std;
+namespace po = boost::program_options;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  string cfg_file;
+  opts.add_options()
+        ("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
+        ("feature_function,F",po::value<vector<string> >()->composing(), "feature function")
+        ("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
+        ("correction_buffers,m", po::value<int>()->default_value(10), "LBFGS correction buffers");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config,c", po::value<string>(&cfg_file), "Configuration file")
+        ("help,?", "Print this help message and exit");
+
+  po::options_description dconfig_options, dcmdline_options;
+  dconfig_options.add(opts);
+  dcmdline_options.add(dconfig_options).add(clo);
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (cfg_file.size() > 0) {
+    ReadFile rf(cfg_file);
+    po::store(po::parse_config_file(*rf.stream(), dconfig_options), *conf);
+  }
+  if (conf->count("help")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+struct TrainingInstance {
+  TaggedSentence ts;
+  EdgeSubset tree;
+  SparseVector<weight_t> features;
+};
+
+void ReadTraining(const string& fname, vector<TrainingInstance>* corpus, int rank = 0, int size = 1) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  string err;
+  int lc = 0;
+  bool flag = false;
+  while(getline(in, line)) {
+    ++lc;
+    if ((lc-1) % size != rank) continue;
+    if (rank == 0 && lc % 10 == 0) { cerr << '.' << flush; flag = true; }
+    if (rank == 0 && lc % 400 == 0) { cerr << " [" << lc << "]\n"; flag = false; }
+    size_t pos = line.rfind('\t');
+    assert(pos != string::npos);
+    picojson::value obj;
+    picojson::parse(obj, line.begin() + pos, line.end(), &err);
+    if (err.size() > 0) { cerr << "JSON parse error in " << lc << ": " << err << endl; abort(); }
+    corpus->push_back(TrainingInstance());
+    TrainingInstance& cur = corpus->back();
+    TaggedSentence& ts = cur.ts;
+    EdgeSubset& tree = cur.tree;
+    assert(obj.is<picojson::object>());
+    const picojson::object& d = obj.get<picojson::object>();
+    const picojson::array& ta = d.find("tokens")->second.get<picojson::array>();
+    for (unsigned i = 0; i < ta.size(); ++i) {
+      ts.words.push_back(TD::Convert(ta[i].get<picojson::array>()[0].get<string>()));
+      ts.pos.push_back(TD::Convert(ta[i].get<picojson::array>()[1].get<string>()));
+    }
+    const picojson::array& da = d.find("deps")->second.get<picojson::array>();
+    for (unsigned i = 0; i < da.size(); ++i) {
+      const picojson::array& thm = da[i].get<picojson::array>();
+      // get dep type here
+      short h = thm[2].get<double>();
+      short m = thm[1].get<double>();
+      if (h < 0)
+        tree.roots.push_back(m);
+      else
+        tree.h_m_pairs.push_back(make_pair(h,m));
+    }
+    //cerr << TD::GetString(ts.words) << endl << TD::GetString(ts.pos) << endl << tree << endl;
+  }
+  if (flag) cerr << "\nRead " << lc << " training instances\n";
+}
+
+void AddFeatures(double prob, const SparseVector<double>& fmap, vector<double>* g) {
+  for (SparseVector<double>::const_iterator it = fmap.begin(); it != fmap.end(); ++it)
+    (*g)[it->first] += it->second * prob;
+}
+
+double ApplyRegularizationTerms(const double C,
+                                const vector<double>& weights,
+                                vector<double>* g) {
+  assert(weights.size() == g->size());
+  double reg = 0;
+  for (size_t i = 0; i < weights.size(); ++i) {
+//    const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
+    const double& w_i = weights[i];
+    double& g_i = (*g)[i];
+    reg += C * w_i * w_i;
+    g_i += 2 * C * w_i;
+
+//    reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
+//    g_i += 2 * T * (w_i - prev_w_i);
+  }
+  return reg;
+}
 
 int main(int argc, char** argv) {
+  int rank = 0;
+  int size = 1;
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
   ArcFactoredForest af(5);
-  cerr << af(0,3) << endl;
+  ArcFFRegistry reg;
+  reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>);
+  vector<TrainingInstance> corpus;
+  vector<boost::shared_ptr<ArcFeatureFunction> > ffs;
+  ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty("")));
+  ReadTraining(conf["training_data"].as<string>(), &corpus, rank, size);
+  vector<ArcFactoredForest> forests(corpus.size());
+  SparseVector<double> empirical;
+  bool flag = false;
+  for (int i = 0; i < corpus.size(); ++i) {
+    TrainingInstance& cur = corpus[i];
+    if (rank == 0 && (i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
+    if (rank == 0 && (i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
+    for (int fi = 0; fi < ffs.size(); ++fi) {
+      ArcFeatureFunction& ff = *ffs[fi];
+      ff.PrepareForInput(cur.ts);
+      SparseVector<weight_t> efmap;
+      for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
+        efmap.clear();
+        ff.EgdeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
+                        cur.tree.h_m_pairs[j].second,
+                        &efmap);
+        cur.features += efmap;
+      }
+      for (int j = 0; j < cur.tree.roots.size(); ++j) {
+        efmap.clear();
+        ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
+        cur.features += efmap;
+      }
+    }
+    empirical += cur.features;
+    forests[i].resize(cur.ts.words.size());
+    forests[i].ExtractFeatures(cur.ts, ffs);
+  }
+  if (flag) cerr << endl;
+  //cerr << "EMP: " << empirical << endl; //DE
+  vector<weight_t> weights(FD::NumFeats(), 0.0);
+  vector<weight_t> g(FD::NumFeats(), 0.0);
+  cerr << "features initialized\noptimizing...\n";
+  boost::shared_ptr<BatchOptimizer> o;
+  o.reset(new LBFGSOptimizer(g.size(), conf["correction_buffers"].as<int>()));
+  int iterations = 1000;
+  for (int iter = 0; iter < iterations; ++iter) {
+    cerr << "ITERATION " << iter << " " << flush;
+    fill(g.begin(), g.end(), 0.0);
+    for (SparseVector<double>::const_iterator it = empirical.begin(); it != empirical.end(); ++it)
+      g[it->first] = -it->second;
+    double obj = -empirical.dot(weights);
+    // SparseVector<double> mfm;  //DE
+    for (int i = 0; i < corpus.size(); ++i) {
+      forests[i].Reweight(weights);
+      double logz;
+      forests[i].EdgeMarginals(&logz);
+      //cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -logz << "  OO= " << (-corpus[i].features.dot(weights) - logz) << endl;
+      obj -= logz;
+      int num_words = corpus[i].ts.words.size();
+      for (int h = -1; h < num_words; ++h) {
+        for (int m = 0; m < num_words; ++m) {
+          if (h == m) continue;
+          const ArcFactoredForest::Edge& edge = forests[i](h,m);
+          const SparseVector<weight_t>& fmap = edge.features;
+          double prob = edge.edge_prob.as_float();
+          if (prob < -0.000001) { cerr << "Prob < 0: " << prob << endl; prob = 0; }
+          if (prob > 1.000001) { cerr << "Prob > 1: " << prob << endl; prob = 1; }
+          AddFeatures(prob, fmap, &g);
+          //mfm += fmap * prob;  // DE
+        }
+      }
+    }
+    //cerr << endl << "E: " << empirical << endl;  // DE
+    //cerr << "M: " << mfm << endl;  // DE
+    double r = ApplyRegularizationTerms(conf["regularization_strength"].as<double>(), weights, &g);
+    double gnorm = 0;
+    for (int i = 0; i < g.size(); ++i)
+      gnorm += g[i]*g[i];
+    cerr << "OBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm) << endl;
+    obj += r;
+    assert(obj >= 0);
+    o->Optimize(obj, g, &weights);
+    Weights::ShowLargestFeatures(weights);
+    if (o->HasConverged()) { cerr << "CONVERGED\n"; break; }
+  }
   return 0;
 }