lbl preliminary clean up

1 files changed, 54 insertions, 30 deletions

diff --git a/training/lbl_model.cc b/training/lbl_model.cc
index 72d80a56..ccd29255 100644
--- a/training/lbl_model.cc
+++ b/training/lbl_model.cc
@@ -6,6 +6,7 @@
 #else
 
 #include <cmath>
+#include <set>
 
 #include <boost/program_options.hpp>
 #include <boost/program_options/variables_map.hpp>
@@ -20,10 +21,17 @@
 namespace po = boost::program_options;
 using namespace std;
 
+#define kDIMENSIONS 10
+typedef Eigen::Matrix<float, kDIMENSIONS, 1> RVector;
+typedef Eigen::Matrix<float, 1, kDIMENSIONS> RTVector;
+typedef Eigen::Matrix<float, kDIMENSIONS, kDIMENSIONS> TMatrix;
+vector<RVector> r_src, r_trg;
+
 bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
-        ("iterations,i",po::value<unsigned>()->default_value(5),"Number of iterations of training")
+        ("input,i",po::value<string>(),"Input file")
+        ("iterations,I",po::value<unsigned>()->default_value(1000),"Number of iterations of training")
         ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (0 = uniform, >0 sharpens)")
         ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model");
   po::options_description clo("Command line options");
@@ -42,7 +50,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::notify(*conf);
 
   if (argc < 2 || conf->count("help")) {
-    cerr << "Usage " << argv[0] << " [OPTIONS] corpus.fr-en\n";
+    cerr << "Usage " << argv[0] << " [OPTIONS] -i corpus.fr-en\n";
     cerr << dcmdline_options << endl;
     return false;
   }
@@ -52,33 +60,32 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
 int main(int argc, char** argv) {
   po::variables_map conf;
   if (!InitCommandLine(argc, argv, &conf)) return 1;
-  const string fname = argv[argc - 1];
+  const string fname = conf["input"].as<string>();
   const int ITERATIONS = conf["iterations"].as<unsigned>();
   const double diagonal_tension = conf["diagonal_tension"].as<double>();
+  if (diagonal_tension < 0.0) {
+    cerr << "Invalid value for diagonal_tension: must be >= 0\n";
+    return 1;
+  }
   string testset;
   if (conf.count("testset")) testset = conf["testset"].as<string>();
 
-  double tot_len_ratio = 0;
-  double mean_srclen_multiplier = 0;
+  int lc = 0;
   vector<double> unnormed_a_i;
-  for (int iter = 0; iter < ITERATIONS; ++iter) {
-    cerr << "ITERATION " << (iter + 1) << endl;
+  string line;
+  string ssrc, strg;
+  bool flag = false;
+  Lattice src, trg;
+  set<WordID> vocab_e;
+  { // read through corpus, initialize int map, check lines are good
+    cerr << "INITIAL READ OF " << fname << endl;
     ReadFile rf(fname);
     istream& in = *rf.stream();
-    double likelihood = 0;
-    double denom = 0.0;
-    int lc = 0;
-    bool flag = false;
-    string line;
-    string ssrc, strg;
-    while(true) {
-      getline(in, line);
-      if (!in) break;
+    while(getline(in, line)) {
       ++lc;
       if (lc % 1000 == 0) { cerr << '.'; flag = true; }
       if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
       ParseTranslatorInput(line, &ssrc, &strg);
-      Lattice src, trg;
       LatticeTools::ConvertTextToLattice(ssrc, &src);
       LatticeTools::ConvertTextToLattice(strg, &trg);
       if (src.size() == 0 || trg.size() == 0) {
@@ -88,37 +95,54 @@ int main(int argc, char** argv) {
       }
       if (src.size() > unnormed_a_i.size())
         unnormed_a_i.resize(src.size());
-      if (iter == 0)
-        tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size());
+      for (unsigned i = 0; i < trg.size(); ++i) {
+        assert(trg[i].size() == 1);
+        vocab_e.insert(trg[i][0].label);
+      }
+    }
+  }
+  if (flag) cerr << endl;
+
+  // do optimization
+  for (int iter = 0; iter < ITERATIONS; ++iter) {
+    cerr << "ITERATION " << (iter + 1) << endl;
+    ReadFile rf(fname);
+    istream& in = *rf.stream();
+    double likelihood = 0;
+    double denom = 0.0;
+    lc = 0;
+    flag = false;
+    while(true) {
+      getline(in, line);
+      if (!in) break;
+      ++lc;
+      if (lc % 1000 == 0) { cerr << '.'; flag = true; }
+      if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
+      ParseTranslatorInput(line, &ssrc, &strg);
+      LatticeTools::ConvertTextToLattice(ssrc, &src);
+      LatticeTools::ConvertTextToLattice(strg, &trg);
       denom += trg.size();
       vector<double> probs(src.size() + 1);
-      bool first_al = true;  // used for write_alignments
       for (int j = 0; j < trg.size(); ++j) {
         const WordID& f_j = trg[j][0].label;
         double sum = 0;
         const double j_over_ts = double(j) / trg.size();
-        double prob_a_i = 1.0 / src.size();
         double az = 0;
         for (int ta = 0; ta < src.size(); ++ta) {
           unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
           az += unnormed_a_i[ta];
         }
         for (int i = 1; i <= src.size(); ++i) {
-          prob_a_i = unnormed_a_i[i-1] / az;
+          const double prob_a_i = unnormed_a_i[i-1] / az;
+          // TODO
           probs[i] = 1; // tt.prob(src[i-1][0].label, f_j) * prob_a_i;
           sum += probs[i];
         }
       }
     }
-
-    // log(e) = 1.0
-    double base2_likelihood = likelihood / log(2);
-
     if (flag) { cerr << endl; }
-    if (iter == 0) {
-      mean_srclen_multiplier = tot_len_ratio / lc;
-      cerr << "expected target length = source length * " << mean_srclen_multiplier << endl;
-    }
+
+    const double base2_likelihood = likelihood / log(2);
     cerr << "  log_e likelihood: " << likelihood << endl;
     cerr << "  log_2 likelihood: " << base2_likelihood << endl;
     cerr << "   cross entropy: " << (-base2_likelihood / denom) << endl;