add generative word alignment model and primitive EM trainer. Model 1 and HMM are supported, without NULL source words

1 files changed, 194 insertions, 0 deletions

diff --git a/training/mr_em_adapted_reduce.cc b/training/mr_em_adapted_reduce.cc
new file mode 100644
index 00000000..52387e7f
--- /dev/null
+++ b/training/mr_em_adapted_reduce.cc
@@ -0,0 +1,194 @@
+#include <iostream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "config.h"
+#ifdef HAVE_BOOST_DIGAMMA
+#include <boost/math/special_functions/digamma.hpp>
+using boost::math::digamma;
+#endif
+
+#include "filelib.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+#ifndef HAVE_BOOST_DIGAMMA
+#warning Using Mark Johnsons digamma()
+double digamma(double x) {
+  double result = 0, xx, xx2, xx4;
+  assert(x > 0);
+  for ( ; x < 7; ++x)
+    result -= 1/x;
+  x -= 1.0/2.0;
+  xx = 1.0/x;
+  xx2 = xx*xx;
+  xx4 = xx2*xx2;
+  result += log(x)+(1./24.)*xx2-(7.0/960.0)*xx4+(31.0/8064.0)*xx4*xx2-(127.0/30720.0)*xx4*xx4;
+  return result;
+}
+#endif
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("optimization_method,m", po::value<string>()->default_value("em"), "Optimization method (em, vb)")
+        ("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)");
+  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")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+double NoZero(const double& x) {
+  if (x) return x;
+  return 1e-35;
+}
+
+void Maximize(const bool use_vb,
+              const double& alpha,
+              const int total_event_types,
+              SparseVector<double>* pc) {
+  const SparseVector<double>& counts = *pc;
+
+  if (use_vb)
+    assert(total_event_types >= counts.num_active());
+
+  double tot = 0;
+  for (SparseVector<double>::const_iterator it = counts.begin();
+       it != counts.end(); ++it)
+    tot += it->second;
+//  cerr << " = " << tot << endl;
+  assert(tot > 0.0);
+  double ltot = log(tot);
+  if (use_vb)
+    ltot = digamma(tot + total_event_types * alpha);
+  for (SparseVector<double>::const_iterator it = counts.begin();
+       it != counts.end(); ++it) {
+    if (use_vb) {
+      pc->set_value(it->first, NoZero(digamma(it->second + alpha) - ltot));
+    } else {
+      pc->set_value(it->first, NoZero(log(it->second) - ltot));
+    }
+  }
+#if 0
+  if (counts.num_active() < 50) {
+    for (SparseVector<double>::const_iterator it = counts.begin();
+         it != counts.end(); ++it) {
+      cerr << " p(" << FD::Convert(it->first) << ")=" << exp(it->second);
+    }
+    cerr << endl;
+  }
+#endif
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+
+  const bool use_b64 = conf["input_format"].as<string>() == "b64";
+  const bool use_vb = conf["optimization_method"].as<string>() == "vb";
+  const double alpha = 1e-09;
+  if (use_vb)
+    cerr << "Using variational Bayes, make sure alphas are set\n";
+
+  const string s_obj = "**OBJ**";
+  // E-step
+  string cur_key = "";
+  SparseVector<double> acc;
+  double logprob = 0;
+  while(cin) {
+    string line;
+    getline(cin, line);
+    if (line.empty()) continue;
+    int feat;
+    double val;
+    size_t i = line.find("\t");
+    const string key = line.substr(0, i);
+    assert(i != string::npos);
+    ++i;
+    if (key != cur_key) {
+      if  (cur_key.size() > 0) {
+        // TODO shouldn't be num_active, should be total number
+        // of events
+        Maximize(use_vb, alpha, acc.num_active(), &acc);
+        cout << cur_key << '\t';
+        if (use_b64)
+          B64::Encode(0.0, acc, &cout);
+        else
+          cout << acc;
+        cout << endl;
+        acc.clear();
+      }
+      cur_key = key;
+    }
+    if (use_b64) {
+      SparseVector<double> g;
+      double obj;
+      if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
+        cerr << "B64 decoder returned error, skipping!\n";
+        continue;
+      }
+      logprob += obj;
+      acc += g;
+    } else {       // text encoding - your counts will not be accurate!
+      while (i < line.size()) {
+        size_t start = i;
+        while (line[i] != '=' && i < line.size()) ++i;
+        if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
+        string fname = line.substr(start, i - start);
+        if (fname == s_obj) {
+          feat = -1;
+        } else {
+          feat = FD::Convert(line.substr(start, i - start));
+        }
+        ++i;
+        start = i;
+        while (line[i] != ';' && i < line.size()) ++i;
+        if (i - start == 0) continue;
+        val = atof(line.substr(start, i - start).c_str());
+        ++i;
+        if (feat == -1) {
+          logprob += val;
+        } else {
+          acc.add_value(feat, val);
+        }
+      }
+    }
+  }
+  // TODO shouldn't be num_active, should be total number
+  // of events
+  Maximize(use_vb, alpha, acc.num_active(), &acc);
+  cout << cur_key << '\t';
+  if (use_b64)
+    B64::Encode(0.0, acc, &cout);
+  else
+    cout << acc;
+  cout << endl << flush;
+
+  cerr << "LOGPROB: " << logprob << endl;
+
+  return 0;
+}