cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 228 deletions

diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
deleted file mode 100644
index a78df600..00000000
--- a/rst_parser/mst_train.cc
+++ /dev/null
@@ -1,228 +0,0 @@
-#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) {
-  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>::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;
-}
-