1 files changed, 144 insertions, 0 deletions
diff --git a/rst_parser/rst_train.cc b/rst_parser/rst_train.cc
new file mode 100644
index 00000000..16673cdc
--- /dev/null
+++ b/rst_parser/rst_train.cc
@@ -0,0 +1,144 @@
+#include "arc_factored.h"
+
+#include <vector>
+#include <iostream>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "timing_stats.h"
+#include "arc_ff.h"
+#include "dep_training.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "tdict.h"
+#include "weights.h"
+#include "rst.h"
+#include "global_ff.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)")
+        ("q_weights,q",po::value<string>(), "Arc-factored weights for proposal distribution")
+        ("samples,n",po::value<unsigned>()->default_value(1000), "Number of samples");
+  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);
+  }
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  vector<weight_t> qweights(FD::NumFeats(), 0.0);
+  Weights::InitFromFile(conf["q_weights"].as<string>(), &qweights);
+  vector<TrainingInstance> corpus;
+  ArcFeatureFunctions ffs;
+  GlobalFeatureFunctions gff;
+  TrainingInstance::ReadTraining(conf["training_data"].as<string>(), &corpus);
+  vector<ArcFactoredForest> forests(corpus.size());
+  vector<prob_t> zs(corpus.size());
+  SparseVector<double> empirical;
+  bool flag = false;
+  for (int i = 0; i < corpus.size(); ++i) {
+    TrainingInstance& cur = corpus[i];
+    if ((i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
+    if ((i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
+    SparseVector<weight_t> efmap;
+    ffs.PrepareForInput(cur.ts);
+    gff.PrepareForInput(cur.ts);
+    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;
+    }
+    efmap.clear();
+    gff.Features(cur.ts, cur.tree, &efmap);
+    cur.features += efmap;
+    empirical += cur.features;
+    forests[i].resize(cur.ts.words.size());
+    forests[i].ExtractFeatures(cur.ts, ffs);
+    forests[i].Reweight(qweights);
+    forests[i].EdgeMarginals(&zs[i]);
+    zs[i] = prob_t::One() / zs[i];
+    // cerr << zs[i] << endl;
+    forests[i].Reweight(qweights);    // EdgeMarginals overwrites edge_prob
+  }
+  if (flag) cerr << endl;
+  MT19937 rng;
+  SparseVector<double> model_exp;
+  SparseVector<double> weights;
+  Weights::InitSparseVector(qweights, &weights);
+  int samples = conf["samples"].as<unsigned>();
+  for (int i = 0; i < corpus.size(); ++i) {
+#if 0
+    forests[i].EdgeMarginals();
+    model_exp.clear();
+    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();
+        model_exp += fmap * prob;
+      }
+    }
+    cerr << "TRUE EXP: " << model_exp << endl;
+    forests[i].Reweight(weights);
+#endif
+
+    TreeSampler ts(forests[i]);
+    prob_t zhat = prob_t::Zero();
+    SparseVector<prob_t> sampled_exp;
+    for (int n = 0; n < samples; ++n) {
+      EdgeSubset tree;
+      ts.SampleRandomSpanningTree(&tree, &rng);
+      SparseVector<double> qfeats, gfeats;
+      tree.ExtractFeatures(corpus[i].ts, ffs, &qfeats);
+      prob_t u; u.logeq(qfeats.dot(qweights));
+      const prob_t q = u / zs[i];  // proposal mass
+      gff.Features(corpus[i].ts, tree, &gfeats);
+      SparseVector<double> tot_feats = qfeats + gfeats;
+      u.logeq(tot_feats.dot(weights));
+      prob_t w = u / q;
+      zhat += w;
+      for (SparseVector<double>::const_iterator it = tot_feats.begin(); it != tot_feats.end(); ++it)
+        sampled_exp.add_value(it->first, w * prob_t(it->second));
+    }
+    sampled_exp /= zhat;
+    SparseVector<double> tot_m;
+    for (SparseVector<prob_t>::const_iterator it = sampled_exp.begin(); it != sampled_exp.end(); ++it)
+      tot_m.add_value(it->first, it->second.as_float());
+    //cerr << "DIFF: " << (tot_m - corpus[i].features) << endl;
+    const double eta = 0.03;
+    weights -= (tot_m - corpus[i].features) * eta;
+  }
+  cerr << "WEIGHTS.\n";
+  cerr << weights << endl;
+  return 0;
+}
+