major restructure of the training code

2 files changed, 0 insertions, 315 deletions

diff --git a/mira/Makefile.am b/mira/Makefile.am
deleted file mode 100644
index 3f8f17cd..00000000
--- a/mira/Makefile.am
+++ /dev/null
@@ -1,6 +0,0 @@
-bin_PROGRAMS = kbest_mira
-
-kbest_mira_SOURCES = kbest_mira.cc
-kbest_mira_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/klm/search/libksearch.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc
deleted file mode 100644
index 8b7993dd..00000000
--- a/mira/kbest_mira.cc
+++ /dev/null
@@ -1,309 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-#include <tr1/memory>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "hg_sampler.h"
-#include "sentence_metadata.h"
-#include "scorer.h"
-#include "verbose.h"
-#include "viterbi.h"
-#include "hg.h"
-#include "prob.h"
-#include "kbest.h"
-#include "ff_register.h"
-#include "decoder.h"
-#include "filelib.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-#include "sampler.h"
-
-using namespace std;
-namespace po = boost::program_options;
-
-bool invert_score;
-std::tr1::shared_ptr<MT19937> rng;
-
-void RandomPermutation(int len, vector<int>* p_ids) {
-  vector<int>& ids = *p_ids;
-  ids.resize(len);
-  for (int i = 0; i < len; ++i) ids[i] = i;
-  for (int i = len; i > 0; --i) {
-    int j = rng->next() * i;
-    if (j == i) i--;
-    swap(ids[i-1], ids[j]);
-  }  
-}
-
-bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("input_weights,w",po::value<string>(),"Input feature weights file")
-        ("source,i",po::value<string>(),"Source file for development set")
-        ("passes,p", po::value<int>()->default_value(15), "Number of passes through the training data")
-        ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)")
-        ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)")
-        ("max_step_size,C", po::value<double>()->default_value(0.01), "regularization strength (C)")
-        ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by")
-        ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles")
-        ("sample_forest,f", "Instead of a k-best list, sample k hypotheses from the decoder's forest")
-        ("sample_forest_unit_weight_vector,x", "Before sampling (must use -f option), rescale the weight vector used so it has unit length; this may improve the quality of the samples")
-        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
-        ("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("input_weights") || !conf->count("source") || !conf->count("decoder_config") || !conf->count("reference")) {
-    cerr << dcmdline_options << endl;
-    return false;
-  }
-  return true;
-}
-
-static const double kMINUS_EPSILON = -1e-6;
-
-struct HypothesisInfo {
-  SparseVector<double> features;
-  double mt_metric;
-};
-
-struct GoodBadOracle {
-  std::tr1::shared_ptr<HypothesisInfo> good;
-  std::tr1::shared_ptr<HypothesisInfo> bad;
-};
-
-struct TrainingObserver : public DecoderObserver {
-  TrainingObserver(const int k, const DocScorer& d, bool sf, vector<GoodBadOracle>* o) : ds(d), oracles(*o), kbest_size(k), sample_forest(sf) {}
-  const DocScorer& ds;
-  vector<GoodBadOracle>& oracles;
-  std::tr1::shared_ptr<HypothesisInfo> cur_best;
-  const int kbest_size;
-  const bool sample_forest;
-
-  const HypothesisInfo& GetCurrentBestHypothesis() const {
-    return *cur_best;
-  }
-
-  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
-    UpdateOracles(smeta.GetSentenceID(), *hg);
-  }
-
-  std::tr1::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) {
-    std::tr1::shared_ptr<HypothesisInfo> h(new HypothesisInfo);
-    h->features = feats;
-    h->mt_metric = score;
-    return h;
-  }
-
-  void UpdateOracles(int sent_id, const Hypergraph& forest) {
-    std::tr1::shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good;
-    std::tr1::shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad;
-    cur_bad.reset();  // TODO get rid of??
-
-    if (sample_forest) {
-      vector<WordID> cur_prediction;
-      ViterbiESentence(forest, &cur_prediction);
-      float sentscore = ds[sent_id]->ScoreCandidate(cur_prediction)->ComputeScore();
-      cur_best = MakeHypothesisInfo(ViterbiFeatures(forest), sentscore);
-
-      vector<HypergraphSampler::Hypothesis> samples;
-      HypergraphSampler::sample_hypotheses(forest, kbest_size, &*rng, &samples);
-      for (unsigned i = 0; i < samples.size(); ++i) {
-        sentscore = ds[sent_id]->ScoreCandidate(samples[i].words)->ComputeScore();
-        if (invert_score) sentscore *= -1.0;
-        if (!cur_good || sentscore > cur_good->mt_metric)
-          cur_good = MakeHypothesisInfo(samples[i].fmap, sentscore);
-        if (!cur_bad || sentscore < cur_bad->mt_metric)
-          cur_bad = MakeHypothesisInfo(samples[i].fmap, sentscore);
-      }
-    } else {
-      KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size);
-      for (int i = 0; i < kbest_size; ++i) {
-        const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
-          kbest.LazyKthBest(forest.nodes_.size() - 1, i);
-        if (!d) break;
-        float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore();
-        if (invert_score) sentscore *= -1.0;
-        // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl;
-        if (i == 0)
-          cur_best = MakeHypothesisInfo(d->feature_values, sentscore);
-        if (!cur_good || sentscore > cur_good->mt_metric)
-          cur_good = MakeHypothesisInfo(d->feature_values, sentscore);
-        if (!cur_bad || sentscore < cur_bad->mt_metric)
-          cur_bad = MakeHypothesisInfo(d->feature_values, sentscore);
-      }
-      //cerr << "GOOD: " << cur_good->mt_metric << endl;
-      //cerr << " CUR: " << cur_best->mt_metric << endl;
-      //cerr << " BAD: " << cur_bad->mt_metric << endl;
-    }
-  }
-};
-
-void ReadTrainingCorpus(const string& fname, vector<string>* c) {
-  ReadFile rf(fname);
-  istream& in = *rf.stream();
-  string line;
-  while(in) {
-    getline(in, line);
-    if (!in) break;
-    c->push_back(line);
-  }
-}
-
-bool ApproxEqual(double a, double b) {
-  if (a == b) return true;
-  return (fabs(a-b)/fabs(b)) < 0.000001;
-}
-
-int main(int argc, char** argv) {
-  register_feature_functions();
-  SetSilent(true);  // turn off verbose decoder output
-
-  po::variables_map conf;
-  if (!InitCommandLine(argc, argv, &conf)) return 1;
-
-  if (conf.count("random_seed"))
-    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    rng.reset(new MT19937);
-  const bool sample_forest = conf.count("sample_forest") > 0;
-  const bool sample_forest_unit_weight_vector = conf.count("sample_forest_unit_weight_vector") > 0;
-  if (sample_forest_unit_weight_vector && !sample_forest) {
-    cerr << "Cannot --sample_forest_unit_weight_vector without --sample_forest" << endl;
-    return 1;
-  }
-  vector<string> corpus;
-  ReadTrainingCorpus(conf["source"].as<string>(), &corpus);
-  const string metric_name = conf["mt_metric"].as<string>();
-  ScoreType type = ScoreTypeFromString(metric_name);
-  if (type == TER) {
-    invert_score = true;
-  } else {
-    invert_score = false;
-  }
-  DocScorer ds(type, conf["reference"].as<vector<string> >(), "");
-  cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl;
-  if (ds.size() != corpus.size()) {
-    cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n";
-    return 1;
-  }
-
-  ReadFile ini_rf(conf["decoder_config"].as<string>());
-  Decoder decoder(ini_rf.stream());
-
-  // load initial weights
-  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
-  SparseVector<weight_t> lambdas;
-  Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights);
-  Weights::InitSparseVector(dense_weights, &lambdas);
-
-  const double max_step_size = conf["max_step_size"].as<double>();
-  const double mt_metric_scale = conf["mt_metric_scale"].as<double>();
-
-  assert(corpus.size() > 0);
-  vector<GoodBadOracle> oracles(corpus.size());
-
-  TrainingObserver observer(conf["k_best_size"].as<int>(), ds, sample_forest, &oracles);
-  int cur_sent = 0;
-  int lcount = 0;
-  int normalizer = 0;
-  double tot_loss = 0;
-  int dots = 0;
-  int cur_pass = 0;
-  SparseVector<double> tot;
-  tot += lambdas;          // initial weights
-  normalizer++;            // count for initial weights
-  int max_iteration = conf["passes"].as<int>() * corpus.size();
-  string msg = "# MIRA tuned weights";
-  string msga = "# MIRA tuned weights AVERAGED";
-  vector<int> order;
-  RandomPermutation(corpus.size(), &order);
-  while (lcount <= max_iteration) {
-    lambdas.init_vector(&dense_weights);
-    if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
-    if (corpus.size() == cur_sent) {
-      cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
-      Weights::ShowLargestFeatures(dense_weights);
-      cur_sent = 0;
-      tot_loss = 0;
-      dots = 0;
-      ostringstream os;
-      os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz";
-      SparseVector<double> x = tot;
-      x /= normalizer;
-      ostringstream sa;
-      sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz";
-      x.init_vector(&dense_weights);
-      Weights::WriteToFile(os.str(), dense_weights, true, &msg);
-      ++cur_pass;
-      RandomPermutation(corpus.size(), &order);
-    }
-    if (cur_sent == 0) {
-      cerr << "PASS " << (lcount / corpus.size() + 1) << endl;
-    }
-    decoder.SetId(order[cur_sent]);
-    double sc = 1.0;
-    if (sample_forest_unit_weight_vector) {
-      sc = lambdas.l2norm();
-      if (sc > 0) {
-        for (unsigned i = 0; i < dense_weights.size(); ++i)
-          dense_weights[i] /= sc;
-      }
-    }
-    decoder.Decode(corpus[order[cur_sent]], &observer);  // update oracles
-    if (sc && sc != 1.0) {
-      for (unsigned i = 0; i < dense_weights.size(); ++i)
-        dense_weights[i] *= sc;
-    }
-    const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis();
-    const HypothesisInfo& cur_good = *oracles[order[cur_sent]].good;
-    const HypothesisInfo& cur_bad = *oracles[order[cur_sent]].bad;
-    tot_loss += cur_hyp.mt_metric;
-    if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) {
-      const double loss = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights) +
-          mt_metric_scale * (cur_good.mt_metric - cur_bad.mt_metric);
-      //cerr << "LOSS: " << loss << endl;
-      if (loss > 0.0) {
-        SparseVector<double> diff = cur_good.features;
-        diff -= cur_bad.features;
-        double step_size = loss / diff.l2norm_sq();
-        //cerr << loss << " " << step_size << " " << diff << endl;
-        if (step_size > max_step_size) step_size = max_step_size;
-        lambdas += (cur_good.features * step_size);
-        lambdas -= (cur_bad.features * step_size);
-        //cerr << "L: " << lambdas << endl;
-      }
-    }
-    tot += lambdas;
-    ++normalizer;
-    ++lcount;
-    ++cur_sent;
-  }
-  cerr << endl;
-  Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg);
-  tot /= normalizer;
-  tot.init_vector(dense_weights);
-  msg = "# MIRA tuned weights (averaged vector)";
-  Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg);
-  cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
-  return 0;
-}
-