tool to compute feature expectations in translation charts

1 files changed, 232 insertions, 0 deletions

diff --git a/training/feature_expectations.cc b/training/feature_expectations.cc
new file mode 100644
index 00000000..f1a85495
--- /dev/null
+++ b/training/feature_expectations.cc
@@ -0,0 +1,232 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+#include <tr1/memory>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "verbose.h"
+#include "hg.h"
+#include "prob.h"
+#include "inside_outside.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "filelib.h"
+#include "online_optimizer.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "sampler.h"
+
+#ifdef HAVE_MPI
+#include <boost/mpi/timer.hpp>
+#include <boost/mpi.hpp>
+namespace mpi = boost::mpi;
+#endif
+
+using namespace std;
+namespace po = boost::program_options;
+
+struct FComp {
+  const vector<double>& w_;
+  FComp(const vector<double>& w) : w_(w) {}
+  bool operator()(int a, int b) const {
+    return fabs(w_[a]) > fabs(w_[b]);
+  }
+};
+
+void ShowFeatures(const vector<double>& w) {
+  vector<int> fnums(w.size());
+  for (int i = 0; i < w.size(); ++i)
+    fnums[i] = i;
+  sort(fnums.begin(), fnums.end(), FComp(w));
+  for (vector<int>::iterator i = fnums.begin(); i != fnums.end(); ++i) {
+    if (w[*i]) cout << FD::Convert(*i) << ' ' << w[*i] << endl;
+  }
+}
+
+void ReadConfig(const string& ini, vector<string>* out) {
+  ReadFile rf(ini);
+  istream& in = *rf.stream();
+  while(in) {
+    string line;
+    getline(in, line);
+    if (!in) continue;
+    out->push_back(line);
+  }
+}
+
+void StoreConfig(const vector<string>& cfg, istringstream* o) {
+  ostringstream os;
+  for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; }
+  o->str(os.str());
+}
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("input,i",po::value<string>(),"Corpus of source language sentences")
+        ("weights,w",po::value<string>(),"Input feature weights file")
+        ("decoder_config,c",po::value<string>(), "cdec.ini file");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help,h", "Print this help message and exit");
+  po::options_description dconfig_options, dcmdline_options;
+  dconfig_options.add(opts);
+  dcmdline_options.add(opts).add(clo);
+  
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (conf->count("config")) {
+    ifstream config((*conf)["config"].as<string>().c_str());
+    po::store(po::parse_config_file(config, dconfig_options), *conf);
+  }
+  po::notify(*conf);
+
+  if (conf->count("help") || !conf->count("input") || !conf->count("decoder_config")) {
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c, vector<int>* order) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  int id = 0;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    if (id % size == rank) {
+      c->push_back(line);
+      order->push_back(id);
+    }
+    ++id;
+  }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+  void Reset() {
+    acc_exp.clear();
+    total_complete = 0;
+  } 
+
+  virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
+    cur_model_exp.clear();
+    state = 1;
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    assert(state == 1);
+    state = 2;
+    const prob_t z = InsideOutside<prob_t,
+                                   EdgeProb,
+                                   SparseVector<prob_t>,
+                                   EdgeFeaturesAndProbWeightFunction>(*hg, &cur_model_exp);
+    cur_model_exp /= z;
+    acc_exp += cur_model_exp;
+  }
+
+  virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    cerr << "IGNORING ALIGNMENT FOREST!\n";
+  }
+
+  virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
+    if (state == 2) {
+      ++total_complete;
+    }
+  }
+
+  void GetExpectations(SparseVector<double>* g) const {
+    g->clear();
+    for (SparseVector<prob_t>::const_iterator it = acc_exp.begin(); it != acc_exp.end(); ++it)
+      g->set_value(it->first, it->second);
+  }
+
+  int total_complete;
+  SparseVector<prob_t> cur_model_exp;
+  SparseVector<prob_t> acc_exp;
+  int state;
+};
+
+#ifdef HAVE_MPI
+namespace boost { namespace mpi {
+  template<>
+  struct is_commutative<std::plus<SparseVector<double> >, SparseVector<double> > 
+    : mpl::true_ { };
+} } // end namespace boost::mpi
+#endif
+
+int main(int argc, char** argv) {
+#ifdef HAVE_MPI
+  mpi::environment env(argc, argv);
+  mpi::communicator world;
+  const int size = world.size(); 
+  const int rank = world.rank();
+#else
+  const int size = 1;
+  const int rank = 0;
+#endif
+  if (size > 1) SetSilent(true);  // turn off verbose decoder output
+  register_feature_functions();
+
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf))
+    return 1;
+
+  // load initial weights
+  Weights weights;
+  if (conf.count("weights"))
+    weights.InitFromFile(conf["weights"].as<string>());
+
+  vector<string> corpus;
+  vector<int> ids;
+  ReadTrainingCorpus(conf["input"].as<string>(), rank, size, &corpus, &ids);
+  assert(corpus.size() > 0);
+
+  vector<string> cdec_ini;
+  ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
+  istringstream ini;
+  StoreConfig(cdec_ini, &ini);
+  Decoder decoder(&ini);
+  if (decoder.GetConf()["input"].as<string>() != "-") {
+    cerr << "cdec.ini must not set an input file\n";
+    return 1;
+  }
+
+  SparseVector<double> x;
+  weights.InitSparseVector(&x);
+  TrainingObserver observer;
+
+  weights.InitFromVector(x);
+  vector<double> lambdas;
+  weights.InitVector(&lambdas);
+  decoder.SetWeights(lambdas);
+  observer.Reset();
+  for (unsigned i = 0; i < corpus.size(); ++i) {
+    int id = ids[i];
+    decoder.SetId(id);
+    decoder.Decode(corpus[i], &observer);
+  }
+  SparseVector<double> local_exps, exps;
+  observer.GetExpectations(&local_exps);
+#ifdef HAVE_MPI
+  reduce(world, local_exps, exps, std::plus<SparseVector<double> >(), 0);
+#else
+  exps.swap(local_exps);
+#endif
+
+  weights.InitFromVector(exps);
+  weights.InitVector(&lambdas);
+  ShowFeatures(lambdas);
+
+  return 0;
+}