compute obj, fixes for grammar filter

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@668 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 185 insertions, 0 deletions

diff --git a/training/compute_cllh.cc b/training/compute_cllh.cc
new file mode 100644
index 00000000..f25e17c3
--- /dev/null
+++ b/training/compute_cllh.cc
@@ -0,0 +1,185 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <mpi.h>
+#include <boost/mpi.hpp>
+#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 "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");
+  opts.add_options()
+        ("weights,w",po::value<string>(),"Input feature weights file")
+        ("training_data,t",po::value<string>(),"Training data corpus")
+        ("decoder_config,c",po::value<string>(),"Decoder configuration 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("training_data") || !conf->count("decoder_config")) {
+    cerr << dcmdline_options << endl;
+    MPI::Finalize();
+    exit(1);
+  }
+}
+
+void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c, vector<int>* ids) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  int lc = 0;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    if (lc % size == rank) {
+      c->push_back(line);
+      ids->push_back(lc);
+    }
+    ++lc;
+  }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+  void Reset() {
+    acc_obj = 0;
+  } 
+
+  virtual void NotifyDecodingStart(const SentenceMetadata&) {
+    cur_obj = 0;
+    state = 1;
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
+    assert(state == 1);
+    state = 2;
+    SparseVector<prob_t> cur_model_exp;
+    const prob_t z = InsideOutside<prob_t,
+                                   EdgeProb,
+                                   SparseVector<prob_t>,
+                                   EdgeFeaturesAndProbWeightFunction>(*hg, &cur_model_exp);
+    cur_obj = log(z);
+  }
+
+  // compute "empirical" expectations, numerator of objective
+  virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    assert(state == 2);
+    state = 3;
+    SparseVector<prob_t> ref_exp;
+    const prob_t ref_z = InsideOutside<prob_t,
+                                       EdgeProb,
+                                       SparseVector<prob_t>,
+                                       EdgeFeaturesAndProbWeightFunction>(*hg, &ref_exp);
+
+    double log_ref_z;
+#if 0
+    if (crf_uniform_empirical) {
+      log_ref_z = ref_exp.dot(feature_weights);
+    } else {
+      log_ref_z = log(ref_z);
+    }
+#else
+    log_ref_z = log(ref_z);
+#endif
+
+    // rounding errors means that <0 is too strict
+    if ((cur_obj - log_ref_z) < kMINUS_EPSILON) {
+      cerr << "DIFF. ERR! log_model_z < log_ref_z: " << cur_obj << " " << log_ref_z << endl;
+      exit(1);
+    }
+    assert(!isnan(log_ref_z));
+    acc_obj += (cur_obj - log_ref_z);
+  }
+
+  double acc_obj;
+  double cur_obj;
+  int state;
+};
+
+namespace mpi = boost::mpi;
+
+int main(int argc, char** argv) {
+  mpi::environment env(argc, argv);
+  mpi::communicator world;
+  const int size = world.size(); 
+  const int rank = world.rank();
+  if (size > 1) SetSilent(true);  // turn off verbose decoder output
+  register_feature_functions();
+
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+
+  // load initial weights
+  Weights weights;
+  if (conf.count("weights"))
+    weights.InitFromFile(conf["weights"].as<string>());
+
+  // freeze feature set
+  //const bool freeze_feature_set = conf.count("freeze_feature_set");
+  //if (freeze_feature_set) FD::Freeze();
+
+  // load cdec.ini and set up decoder
+  ReadFile ini_rf(conf["decoder_config"].as<string>());
+  Decoder decoder(ini_rf.stream());
+  if (decoder.GetConf()["input"].as<string>() != "-") {
+    cerr << "cdec.ini must not set an input file\n";
+    abort();
+  }
+
+  vector<string> corpus; vector<int> ids;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
+  assert(corpus.size() > 0);
+  assert(corpus.size() == ids.size());
+
+  vector<double> wv;
+  weights.InitVector(&wv);
+  decoder.SetWeights(wv);
+  TrainingObserver observer;
+  double objective = 0;
+  bool converged = false;
+
+  observer.Reset();
+  if (rank == 0)
+    cerr << "Each processor is decoding " << corpus.size() << " training examples...\n";
+
+  for (int i = 0; i < corpus.size(); ++i) {
+    decoder.SetId(ids[i]);
+    decoder.Decode(corpus[i], &observer);
+  }
+
+  reduce(world, observer.acc_obj, objective, std::plus<double>(), 0);
+
+  if (rank == 0)
+    cout << "OBJECTIVE: " << objective << endl;
+
+  return 0;
+}