rst sampler

1 files changed, 126 insertions, 0 deletions

diff --git a/rst_parser/rst_parse.cc b/rst_parser/rst_parse.cc
new file mode 100644
index 00000000..9cc1359a
--- /dev/null
+++ b/rst_parser/rst_parse.cc
@@ -0,0 +1,126 @@
+#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 "arc_ff_factory.h"
+#include "dep_training.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "tdict.h"
+#include "weights.h"
+#include "rst.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 (multiple permitted)")
+        ("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);
+  ArcFactoredForest af(5);
+  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("")));
+  TrainingInstance::ReadTraining(conf["training_data"].as<string>(), &corpus);
+  vector<ArcFactoredForest> forests(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; }
+    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;
+  vector<weight_t> weights(FD::NumFeats(), 0.0);
+  Weights::InitFromFile(conf["q_weights"].as<string>(), &weights);
+  MT19937 rng;
+  SparseVector<double> model_exp;
+  SparseVector<double> sampled_exp;
+  int samples = conf["samples"].as<unsigned>();
+  for (int i = 0; i < corpus.size(); ++i) {
+    const int num_words = corpus[i].ts.words.size();
+    forests[i].Reweight(weights);
+    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);
+    TreeSampler ts(forests[i]);
+    sampled_exp.clear();
+      //ostringstream os; os << "Samples_" << samples;
+      //Timer t(os.str());
+      for (int n = 0; n < samples; ++n) {
+        EdgeSubset tree;
+        ts.SampleRandomSpanningTree(&tree, &rng);
+        SparseVector<double> feats;
+        tree.ExtractFeatures(corpus[i].ts, ffs, &feats);
+        sampled_exp += feats;
+      }
+      sampled_exp /= samples;
+      cerr << "L2 norm of diff @ " << samples << " samples: " << (model_exp - sampled_exp).l2norm() << endl;
+  }
+  return 0;
+}
+