Merge remote-tracking branch 'upstream/master'

Conflicts:
	Makefile.am
	configure.ac

1 files changed, 218 insertions, 2 deletions

diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index 7b5af4c1..6332693e 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -1,12 +1,228 @@
 #include "arc_factored.h"
 
+#include <vector>
 #include <iostream>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+// #define HAVE_THREAD 1
+#if HAVE_THREAD
+#include <boost/thread.hpp>
+#endif
+
+#include "arc_ff.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "tdict.h"
+#include "dep_training.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)")
+        ("weights,w",po::value<string>(), "Optional starting weights")
+        ("output_every_i_iterations,I",po::value<unsigned>()->default_value(1), "Write weights every I iterations")
+        ("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
+#ifdef HAVE_CMPH
+        ("cmph_perfect_feature_hash,h", po::value<string>(), "Load perfect hash function for features")
+#endif
+#if HAVE_THREAD
+        ("threads,T",po::value<unsigned>()->default_value(1), "Number of threads")
+#endif
+        ("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);
+  }
+}
+
+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;
+}
+
+struct GradientWorker {
+  GradientWorker(int f,
+                 int t,
+                 vector<double>* w,
+                 vector<TrainingInstance>* c,
+                 vector<ArcFactoredForest>* fs) : obj(), weights(*w), from(f), to(t), corpus(*c), forests(*fs), g(w->size()) {}
+  void operator()() {
+    int every = (to - from) / 20;
+    if (!every) every++;
+    for (int i = from; i < to; ++i) {
+      if ((from == 0) && (i + 1) % every == 0) cerr << '.' << flush;
+      const int num_words = corpus[i].ts.words.size();
+      forests[i].Reweight(weights);
+      prob_t z;
+      forests[i].EdgeMarginals(&z);
+      obj -= log(z);
+      //cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -lz << "  OO= " << (-corpus[i].features.dot(weights) - lz) << endl;
+      //cerr << " ZZ = " << zz << endl;
+      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
+        }
+      }
+    }
+  }
+  double obj;
+  vector<double>& weights;
+  const int from, to;
+  vector<TrainingInstance>& corpus;
+  vector<ArcFactoredForest>& forests;
+  vector<double> g; // local gradient
+};
 
 int main(int argc, char** argv) {
-  ArcFactoredForest af(5);
-  cerr << af(0,3) << endl;
+  int rank = 0;
+  int size = 1;
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  if (conf.count("cmph_perfect_feature_hash")) {
+    cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as<string>() << " ...\n";
+    FD::EnableHash(conf["cmph_perfect_feature_hash"].as<string>());
+    cerr << "  " << FD::NumFeats() << " features in map\n";
+  }
+  ArcFeatureFunctions ffs;
+  vector<TrainingInstance> corpus;
+  TrainingInstance::ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus, rank, size);
+  vector<weight_t> weights;
+    Weights::InitFromFile(conf["weights"].as<string>(), &weights);
+  vector<ArcFactoredForest> forests(corpus.size());
+  SparseVector<double> empirical;
+  cerr << "Extracting features...\n";
+  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; }
+    ffs.PrepareForInput(cur.ts);
+    SparseVector<weight_t> efmap;
+    for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
+      efmap.clear();
+      ffs.EdgeFeatures(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();
+      ffs.EdgeFeatures(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
+  weights.resize(FD::NumFeats(), 0.0);
+  vector<weight_t> g(FD::NumFeats(), 0.0);
+  cerr << "features initialized\noptimizing...\n";
+  boost::shared_ptr<BatchOptimizer> o;
+#if HAVE_THREAD
+  unsigned threads = conf["threads"].as<unsigned>();
+  if (threads > corpus.size()) threads = corpus.size();
+#else
+  const unsigned threads = 1;
+#endif
+  int chunk = corpus.size() / threads;
+  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);
+    vector<boost::shared_ptr<GradientWorker> > jobs;
+    for (int from = 0; from < corpus.size(); from += chunk) {
+      int to = from + chunk;
+      if (to > corpus.size()) to = corpus.size();
+      jobs.push_back(boost::shared_ptr<GradientWorker>(new GradientWorker(from, to, &weights, &corpus, &forests)));
+    }
+#if HAVE_THREAD
+    boost::thread_group tg;
+    for (int i = 0; i < threads; ++i)
+      tg.create_thread(boost::ref(*jobs[i]));
+    tg.join_all();
+#else
+    (*jobs[0])();
+#endif
+    for (int i = 0; i < threads; ++i) {
+      obj += jobs[i]->obj;
+      vector<double>& tg = jobs[i]->g;
+      for (unsigned j = 0; j < g.size(); ++j)
+        g[j] += tg[j];
+    }
+    // SparseVector<double> mfm;  //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];
+    ostringstream ll;
+    ll << "ITER=" << (iter+1) << "\tOBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm);
+    cerr << ' ' << ll.str().substr(ll.str().find('\t')+1) << endl;
+    obj += r;
+    assert(obj >= 0);
+    o->Optimize(obj, g, &weights);
+    Weights::ShowLargestFeatures(weights);
+    const bool converged = o->HasConverged();
+    const char* ofname = converged ? "weights.final.gz" : "weights.cur.gz";
+    if (converged || ((iter+1) % conf["output_every_i_iterations"].as<unsigned>()) == 0) {
+      cerr << "writing..." << flush;
+      const string sl = ll.str();
+      Weights::WriteToFile(ofname, weights, true, &sl);
+      cerr << "done" << endl;
+    }
+    if (converged) { cerr << "CONVERGED\n"; break; }
+  }
   return 0;
 }