path: root/dtrain/dtrain.cc

#include "common.h"
#include "kbestget.h"
#include "updater.h"
#include "util.h"

// boost compression
#include <boost/iostreams/device/file.hpp> 
#include <boost/iostreams/filtering_stream.hpp>
#include <boost/iostreams/filter/gzip.hpp>
//#include <boost/iostreams/filter/zlib.hpp>
//#include <boost/iostreams/filter/bzip2.hpp>
using namespace boost::iostreams;

#ifdef DTRAIN_DEBUG
#include "tests.h"
#endif


/*
 * init
 *
 */
bool
init(int argc, char** argv, po::variables_map* cfg)
{
  po::options_description conff( "Configuration File Options" );
  size_t k, N, T, stop;
  string s;
  conff.add_options()
    ( "decoder_config", po::value<string>(),                            "configuration file for cdec" )
    ( "kbest",          po::value<size_t>(&k)->default_value(DTRAIN_DEFAULT_K),         "k for kbest" )
    ( "ngrams",         po::value<size_t>(&N)->default_value(DTRAIN_DEFAULT_N),        "n for Ngrams" )
    ( "filter",         po::value<string>(),                                      "filter kbest list" ) // FIXME
    ( "epochs",         po::value<size_t>(&T)->default_value(DTRAIN_DEFAULT_T),   "# of iterations T" ) 
    ( "input",          po::value<string>(),                                             "input file" )
    ( "scorer",         po::value<string>(&s)->default_value(DTRAIN_DEFAULT_SCORER), "scoring metric" )
    ( "output",         po::value<string>(),                                    "output weights file" )
    ( "stop_after",     po::value<size_t>(&stop)->default_value(0),    "stop after X input sentences" )
    ( "weights_file",   po::value<string>(),       "input weights file (e.g. from previous iteration" );

  po::options_description clo("Command Line Options");
  clo.add_options()
    ( "config,c",         po::value<string>(),              "dtrain config file" )
    ( "quiet,q",          po::value<bool>()->zero_tokens(),           "be quiet" )
    ( "verbose,v",        po::value<bool>()->zero_tokens(),         "be verbose" )
#ifndef DTRAIN_DEBUG
    ;
#else
    ( "test", "run tests and exit");
#endif
  po::options_description config_options, cmdline_options;

  config_options.add(conff);
  cmdline_options.add(clo);
  cmdline_options.add(conff);

  po::store( parse_command_line(argc, argv, cmdline_options), *cfg );
  if ( cfg->count("config") ) {
    ifstream config( (*cfg)["config"].as<string>().c_str() );
    po::store( po::parse_config_file(config, config_options), *cfg );
  }
  po::notify(*cfg);

  if ( !cfg->count("decoder_config") || !cfg->count("input") ) { 
    cerr << cmdline_options << endl;
    return false;
  }
  #ifdef DTRAIN_DEBUG       
  if ( !cfg->count("test") ) {
    cerr << cmdline_options << endl;
    return false;
  }
  #endif
  return true;
}


ostream& _nopos( ostream& out ) { return out << resetiosflags( ios::showpos ); }
ostream& _pos( ostream& out ) { return out << setiosflags( ios::showpos ); }
ostream& _prec2( ostream& out ) { return out << setprecision(2); }
ostream& _prec5( ostream& out ) { return out << setprecision(5); }


/*
 * main
 *
 */
int
main(int argc, char** argv)
{
  // handle most parameters
  po::variables_map cfg;
  if ( ! init(argc, argv, &cfg) ) exit(1); // something is wrong 
#ifdef DTRAIN_DEBUG
  if ( cfg.count("test") ) run_tests(); // run tests and exit 
#endif
  bool quiet = false;
  if ( cfg.count("quiet") ) quiet = true;
  bool verbose = false;  
  if ( cfg.count("verbose") ) verbose = true;
  const size_t k = cfg["kbest"].as<size_t>();
  const size_t N = cfg["ngrams"].as<size_t>(); 
  const size_t T = cfg["epochs"].as<size_t>();
  const size_t stop_after = cfg["stop_after"].as<size_t>();
  if ( !quiet ) {
    cout << endl << "dtrain" << endl << "Parameters:" << endl;
    cout << setw(16) << "k " << k << endl;
    cout << setw(16) << "N " << N << endl;
    cout << setw(16) << "T " << T << endl;
    if ( cfg.count("stop-after") )
      cout << setw(16) << "stop_after " << stop_after << endl;
    if ( cfg.count("weights") )
      cout << setw(16) << "weights " << cfg["weights"].as<string>() << endl;
    cout << setw(16) << "input " << "'" << cfg["input"].as<string>() << "'" << endl;
  }

  // setup decoder, observer
  register_feature_functions();
  SetSilent(true);
  ReadFile ini_rf( cfg["decoder_config"].as<string>() );
  if ( !quiet )
    cout << setw(16) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
  Decoder decoder(ini_rf.stream());
  KBestGetter observer( k );

  // scoring metric/scorer
  string scorer_str = cfg["scorer"].as<string>();
  double (*scorer)( NgramCounts&, const size_t, const size_t, size_t, vector<float> );
  if ( scorer_str == "bleu" ) {
    scorer = &bleu;
  } else if ( scorer_str == "stupid_bleu" ) {
    scorer = &stupid_bleu;
  } else if ( scorer_str == "smooth_bleu" ) {
    scorer = &smooth_bleu;
  } else if ( scorer_str == "approx_bleu" ) {
    scorer = &approx_bleu;
  } else {
    cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
    exit(1);
  }
  // for approx_bleu
  NgramCounts global_counts( N ); // counts for 1 best translations
  size_t global_hyp_len = 0;      // sum hypothesis lengths
  size_t global_ref_len = 0;      // sum reference lengths
  // this is all BLEU implmentations
  vector<float> bleu_weights; // we leave this empty -> 1/N; TODO? 
  if ( !quiet ) cout << setw(16) << "scorer '" << scorer_str << "'" << endl << endl;

  // init weights
  Weights weights;
  if ( cfg.count("weights") ) weights.InitFromFile( cfg["weights"].as<string>() );
  SparseVector<double> lambdas;
  weights.InitSparseVector(&lambdas);
  vector<double> dense_weights;

  // input
  if ( !quiet && !verbose )
    cout << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl;
  string input_fn = cfg["input"].as<string>();
  ifstream input;
  if ( input_fn != "-" ) input.open( input_fn.c_str() );
  string in;
  vector<string> in_split; // input: src\tref\tpsg
  vector<string> ref_tok;  // tokenized reference
  vector<WordID> ref_ids;  // reference as vector of WordID
  string grammar_str;

  // buffer input for t > 0
  vector<string> src_str_buf;           // source strings, TODO? memory
  vector<vector<WordID> > ref_ids_buf;  // references as WordID vecs
  filtering_ostream grammar_buf;        // written to compressed file in /tmp
  // this is for writing the grammar buffer file
  grammar_buf.push( gzip_compressor() );
  char grammar_buf_tmp_fn[] = DTRAIN_TMP_DIR"/dtrain-grammars-XXXXXX";
  mkstemp( grammar_buf_tmp_fn );
  grammar_buf.push( file_sink(grammar_buf_tmp_fn, ios::binary | ios::trunc) );
  
  size_t sid = 0, in_sz = 99999999; // sentence id, input size
  double acc_1best_score = 0., acc_1best_model = 0.;
  vector<vector<double> > scores_per_iter;
  double max_score = 0.;
  size_t best_t = 0;
  bool next = false, stop = false;
  double score = 0.;
  size_t cand_len = 0;
  Scores scores;
  double overall_time = 0.;

  cout << setprecision( 5 );


  for ( size_t t = 0; t < T; t++ ) // T epochs
  {

  time_t start, end;  
  time( &start );

  // actually, we need only need this if t > 0 FIXME
  ifstream grammar_file( grammar_buf_tmp_fn, ios_base::in | ios_base::binary );
  filtering_istream grammar_buf_in;
  grammar_buf_in.push( gzip_decompressor() );
  grammar_buf_in.push( grammar_file );

  // reset average scores
  acc_1best_score = acc_1best_model = 0.;

  sid = 0;                   // reset sentence counter
  
  if ( !quiet ) cout << "Iteration #" << t+1 << " of " << T << "." << endl;
  
  while( true ) {

    // get input from stdin or file
    in.clear();
    next = stop = false; // next iteration, premature stop
    if ( t == 0 ) {    
      if ( input_fn == "-" ) {
        if ( !getline(cin, in) ) next = true;
      } else {
        if ( !getline(input, in) ) next = true; 
      }
    } else {
      if ( sid == in_sz ) next = true; // stop if we reach the end of our input
    }
    // stop after X sentences (but still iterate for those)
    if ( stop_after > 0 && stop_after == sid && !next ) stop = true;
    
    // produce some pretty output
    if ( !quiet && !verbose ) {
        if ( sid == 0 ) cout << " ";
        if ( (sid+1) % (DTRAIN_DOTS) == 0 ) {
            cout << ".";
            cout.flush();
        }
        if ( (sid+1) % (20*DTRAIN_DOTS) == 0) {
            cout << " " << sid+1 << endl;
            if ( !next && !stop ) cout << " ";
        }
        if ( stop ) {
          if ( sid % (20*DTRAIN_DOTS) != 0 ) cout << " " << sid << endl;
          cout << "Stopping after " << stop_after << " input sentences." << endl;
        } else {
          if ( next ) {
            if ( sid % (20*DTRAIN_DOTS) != 0 ) {
              cout << " " << sid << endl;
            }
          }
        }
    }
    
    // next iteration
    if ( next || stop ) break;

    // weights
    dense_weights.clear();
    weights.InitFromVector( lambdas );
    weights.InitVector( &dense_weights );
    decoder.SetWeights( dense_weights );

    switch ( t ) {
      case 0:
        // handling input
        in_split.clear();
        boost::split( in_split, in, boost::is_any_of("\t") );
        // getting reference
        ref_tok.clear(); ref_ids.clear();
        boost::split( ref_tok, in_split[1], boost::is_any_of(" ") );
        register_and_convert( ref_tok, ref_ids );
        ref_ids_buf.push_back( ref_ids );
        // process and set grammar
        grammar_buf << in_split[2] << endl;
        grammar_str = boost::replace_all_copy( in_split[2], " __NEXT_RULE__ ", "\n" );
        grammar_str += "\n";
        decoder.SetSentenceGrammarFromString( grammar_str );
        // decode, kbest
        src_str_buf.push_back( in_split[0] );
        decoder.Decode( in_split[0], &observer );
        break;
      default:
        // get buffered grammar
        string g;
        getline(grammar_buf_in, g);
        grammar_str = boost::replace_all_copy( g, " __NEXT_RULE__ ", "\n" );
        grammar_str += "\n";
        decoder.SetSentenceGrammarFromString( grammar_str );
        // decode, kbest
        decoder.Decode( src_str_buf[sid], &observer );
        break;
    }

    // get kbest list
    KBestList* kb = observer.GetKBest();

    // scoring kbest
    scores.clear();
    if ( t > 0 ) ref_ids = ref_ids_buf[sid];
    for ( size_t i = 0; i < kb->sents.size(); i++ ) {
      NgramCounts counts = make_ngram_counts( ref_ids, kb->sents[i], N );
      // for approx bleu
      if ( scorer_str == "approx_bleu" ) {
        if ( i == 0 ) { // 'context of 1best translations'
          global_counts  += counts;
          global_hyp_len += kb->sents[i].size();
          global_ref_len += ref_ids.size();
          counts.reset();
          cand_len = 0;
        } else {
            cand_len = kb->sents[i].size();
        }
        NgramCounts counts_tmp = global_counts + counts;
        score = scorer( counts_tmp,
                        global_ref_len,
                        global_hyp_len + cand_len, N, bleu_weights );
      } else {
        // other scorers
        cand_len = kb->sents[i].size();
        score = scorer( counts,
                        ref_ids.size(),
                        kb->sents[i].size(), N, bleu_weights );
      }

      if ( i == 0 ) {
        acc_1best_score += score;
        acc_1best_model += kb->scores[i];
      }

      // scorer score and model score
      ScorePair sp( kb->scores[i], score );
      scores.push_back( sp );

      if ( verbose ) {
        cout << "k=" << i+1 << " '" << TD::GetString( ref_ids ) << "'[ref] vs '";
        cout << _prec5 << _nopos << TD::GetString( kb->sents[i] ) << "'[hyp]";
        cout << " [SCORE=" << score << ",model="<< kb->scores[i] << "]" << endl;
        //cout << kb->feats[i] << endl; this is maybe too verbose
      }
    } // Nbest loop
    if ( verbose ) cout << endl;

    // update weights; FIXME others
    SofiaUpdater updater;
    updater.Init( sid, kb->feats, scores );
    updater.Update( lambdas );

    ++sid;

  } // input loop

  if ( t == 0 ) in_sz = sid; // remember size (lines) of input

  // print some stats
  double avg_1best_score = acc_1best_score/(double)in_sz;
  double avg_1best_model = acc_1best_model/(double)in_sz;
  double avg_1best_score_diff, avg_1best_model_diff;
  if ( t > 0 ) {
    avg_1best_score_diff = avg_1best_score - scores_per_iter[t-1][0];
    avg_1best_model_diff = avg_1best_model - scores_per_iter[t-1][1];
  } else {
    avg_1best_score_diff = avg_1best_score;
    avg_1best_model_diff = avg_1best_model;
  }
  cout << _prec5 << _nopos << "(sanity weights Glue=" << dense_weights[FD::Convert( "Glue" )];
  cout << " LexEF=" << dense_weights[FD::Convert( "LexEF" )];
  cout << " LexFE=" << dense_weights[FD::Convert( "LexFE" )] << ")" << endl;
  cout << "     avg score: " << avg_1best_score;
  cout << _pos << " (" << avg_1best_score_diff << ")" << endl;
  cout << _nopos << "avg modelscore: " << avg_1best_model;
  cout << _pos << " (" << avg_1best_model_diff << ")" << endl;
  vector<double> remember_scores;
  remember_scores.push_back( avg_1best_score );
  remember_scores.push_back( avg_1best_model );
  scores_per_iter.push_back( remember_scores );
  if ( avg_1best_score > max_score ) {
    max_score = avg_1best_score;
    best_t = t;
  }

  // close open files
  if ( input_fn != "-" ) input.close();
  close( grammar_buf );
  grammar_file.close();

  time ( &end );
  double time_dif = difftime( end, start );
  overall_time += time_dif;
  if ( !quiet ) {
    cout << _prec2 << _nopos << "(time " << time_dif/60. << " min, ";
    cout << time_dif/(double)in_sz<< " s/S)" << endl;
  }
  
  if ( t+1 != T ) cout << endl;

  } // outer loop

  unlink( grammar_buf_tmp_fn );
  if ( !quiet ) cout << endl << "writing weights file '" << cfg["output"].as<string>() << "' ...";
  weights.WriteToFile( cfg["output"].as<string>(), true );
  if ( !quiet ) cout << "done" << endl;
  
  if ( !quiet ) {
    cout << _prec5 << _nopos << endl << "---" << endl << "Best iteration: ";
    cout << best_t+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
    cout << _prec2 << "This took " << overall_time/60. << " min." << endl;
  }

  return 0;
}