initial check in

1 files changed, 270 insertions, 0 deletions

diff --git a/training/mr_em_train.cc b/training/mr_em_train.cc
new file mode 100644
index 00000000..a15fbe4c
--- /dev/null
+++ b/training/mr_em_train.cc
@@ -0,0 +1,270 @@
+#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 "tdict.h"
+#include "filelib.h"
+#include "trule.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+#ifndef HAVE_BOOST_DIGAMMA
+#warning Using Mark Johnson's 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 SanityCheck(const vector<double>& w) {
+  for (int i = 0; i < w.size(); ++i) {
+    assert(!isnan(w[i]));
+  }
+}
+
+struct FComp {
+  const vector<double>& w_;
+  FComp(const vector<double>& w) : w_(w) {}
+  bool operator()(int a, int b) const {
+    return w_[a] > w_[b];
+  }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+  vector<int> fnums(w.size() - 1);
+  for (int i = 1; i < w.size(); ++i)
+    fnums[i-1] = i;
+  vector<int>::iterator mid = fnums.begin();
+  mid += (w.size() > 10 ? 10 : w.size()) - 1;
+  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+  cerr << "MOST PROBABLE:";
+  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+  }
+  cerr << endl;
+}
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("output,o",po::value<string>()->default_value("-"),"Output log probs file")
+        ("grammar,g",po::value<vector<string> >()->composing(),"SCFG grammar file(s)")
+        ("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") || !conf->count("grammar")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+// describes a multinomial or multinomial with a prior
+// does not contain the parameters- just the list of events
+// and any hyperparameters
+struct MultinomialInfo {
+ MultinomialInfo() : alpha(1.0) {}
+ vector<int> events;      // the events that this multinomial generates
+ double alpha;            // hyperparameter for (optional) Dirichlet prior
+};
+
+typedef map<WordID, MultinomialInfo> ModelDefinition;
+
+void LoadModelEvents(const po::variables_map& conf, ModelDefinition* pm) {
+  ModelDefinition& m = *pm;
+  m.clear();
+  vector<string> gfiles = conf["grammar"].as<vector<string> >();
+  for (int i = 0; i < gfiles.size(); ++i) {
+    ReadFile rf(gfiles[i]);
+    istream& in = *rf.stream();
+    int lc = 0;
+    while(in) {
+      string line;
+      getline(in, line);
+      if (line.empty()) continue;
+      ++lc;
+      TRule r(line, true);
+      const SparseVector<double>& f = r.GetFeatureValues();
+      if (f.num_active() == 0) {
+        cerr << "[WARNING] no feature found in " << gfiles[i] << ':' << lc << endl;
+        continue;
+      }
+      if (f.num_active() > 1) {
+        cerr << "[ERROR] more than one feature found in " << gfiles[i] << ':' << lc << endl;
+        exit(1);
+      }
+      SparseVector<double>::const_iterator it = f.begin();
+      if (it->second != 1.0) {
+        cerr << "[ERROR] feature with value != 1 found in " << gfiles[i] << ':' << lc << endl;
+        exit(1);
+      }
+      m[r.GetLHS()].events.push_back(it->first);
+    }
+  }
+  for (ModelDefinition::iterator it = m.begin(); it != m.end(); ++it) {
+    const vector<int>& v = it->second.events;
+    cerr << "Multinomial [" << TD::Convert(it->first*-1) << "]\n";
+    if (v.size() < 1000) {
+      cerr << "  generates:";    
+      for (int i = 0; i < v.size(); ++i) {
+        cerr << " " << FD::Convert(v[i]);
+      }
+      cerr << endl;
+    }
+  }
+}
+
+void Maximize(const ModelDefinition& m, const bool use_vb, vector<double>* counts) {
+  for (ModelDefinition::const_iterator it = m.begin(); it != m.end(); ++it) {
+    const MultinomialInfo& mult_info = it->second;
+    const vector<int>& events = mult_info.events;
+    cerr << "Multinomial [" << TD::Convert(it->first*-1) << "]";
+    double tot = 0;
+    for (int i = 0; i < events.size(); ++i)
+      tot += (*counts)[events[i]];
+    cerr << " = " << tot << endl;
+    assert(tot > 0.0);
+    double ltot = log(tot);
+    if (use_vb)
+      ltot = digamma(tot + events.size() * mult_info.alpha);
+    for (int i = 0; i < events.size(); ++i) {
+      if (use_vb) {
+        (*counts)[events[i]] = digamma((*counts)[events[i]] + mult_info.alpha) - ltot;
+      } else {
+        (*counts)[events[i]] = log((*counts)[events[i]]) - ltot;
+      }
+    }
+    if (events.size() < 50) {
+      for (int i = 0; i < events.size(); ++i) {
+        cerr << " p(" << FD::Convert(events[i]) << ")=" << exp((*counts)[events[i]]);
+      }
+      cerr << endl;
+    }
+  }
+}
+
+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";
+  if (use_vb)
+    cerr << "Using variational Bayes, make sure alphas are set\n";
+
+  ModelDefinition model_def;
+  LoadModelEvents(conf, &model_def);
+
+  const string s_obj = "**OBJ**";
+  int num_feats = FD::NumFeats();
+  cerr << "Number of features: " << num_feats << endl;
+
+  vector<double> counts(num_feats, 0);
+  double logprob = 0;
+  // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
+  // 0<TAB>**OBJ**=1.1;Feat1=1.0;
+
+  // E-step
+  while(cin) {
+    string line;
+    getline(cin, line);
+    if (line.empty()) continue;
+    int feat;
+    double val;
+    size_t i = line.find("\t");
+    assert(i != string::npos);
+    ++i;
+    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;
+      const SparseVector<double>& cg = g;
+      for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
+        if (it->first >= num_feats) {
+	  cerr << "Unexpected feature: " << FD::Convert(it->first) << endl;
+	  abort();
+        }
+        counts[it->first] += it->second;
+      }
+    } 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));
+          if (feat >= num_feats) {
+	    cerr << "Unexpected feature: " << line.substr(start, i - start) << endl;
+	    abort();
+	  }
+        }
+        ++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 {
+          counts[feat] += val;
+        }
+      }
+    }
+  }
+
+  cerr << "LOGPROB: " << logprob << endl;
+  // M-step
+  Maximize(model_def, use_vb, &counts);
+
+  SanityCheck(counts);
+  ShowLargestFeatures(counts);
+  Weights weights;
+  weights.InitFromVector(counts);
+  weights.WriteToFile(conf["output"].as<string>(), false);
+
+  return 0;
+}