major restructure of the training code

1 files changed, 0 insertions, 657 deletions

diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
deleted file mode 100644
index 18286668..00000000
--- a/dtrain/dtrain.cc
+++ /dev/null
@@ -1,657 +0,0 @@
-#include "dtrain.h"
-
-
-bool
-dtrain_init(int argc, char** argv, po::variables_map* cfg)
-{
-  po::options_description ini("Configuration File Options");
-  ini.add_options()
-    ("input",             po::value<string>()->default_value("-"),                                                   "input file")
-    ("output",            po::value<string>()->default_value("-"),                          "output weights file, '-' for STDOUT")
-    ("input_weights",     po::value<string>(),                                "input weights file (e.g. from previous iteration)")
-    ("decoder_config",    po::value<string>(),                                                      "configuration file for cdec")
-    ("print_weights",     po::value<string>(),                                               "weights to print on each iteration")
-    ("stop_after",        po::value<unsigned>()->default_value(0),                                 "stop after X input sentences")
-    ("tmp",               po::value<string>()->default_value("/tmp"),                                           "temp dir to use")
-    ("keep",              po::value<bool>()->zero_tokens(),                               "keep weights files for each iteration")
-    ("hstreaming",        po::value<string>(),                                   "run in hadoop streaming mode, arg is a task id")
-    ("epochs",            po::value<unsigned>()->default_value(10),                               "# of iterations T (per shard)")
-    ("k",                 po::value<unsigned>()->default_value(100),                            "how many translations to sample")
-    ("sample_from",       po::value<string>()->default_value("kbest"),     "where to sample translations from: 'kbest', 'forest'")
-    ("filter",            po::value<string>()->default_value("uniq"),                          "filter kbest list: 'not', 'uniq'")
-    ("pair_sampling",     po::value<string>()->default_value("XYX"),                 "how to sample pairs: 'all', 'XYX' or 'PRO'")
-    ("hi_lo",             po::value<float>()->default_value(0.1),                   "hi and lo (X) for XYX (default 0.1), <= 0.5")
-    ("pair_threshold",    po::value<score_t>()->default_value(0.),                         "bleu [0,1] threshold to filter pairs")
-    ("N",                 po::value<unsigned>()->default_value(4),                                          "N for Ngrams (BLEU)")
-    ("scorer",            po::value<string>()->default_value("stupid_bleu"),      "scoring: bleu, stupid_, smooth_, approx_, lc_")
-    ("learning_rate",     po::value<weight_t>()->default_value(1.0),                                              "learning rate")
-    ("gamma",             po::value<weight_t>()->default_value(0.),                            "gamma for SVM (0 for perceptron)")
-    ("select_weights",    po::value<string>()->default_value("last"),     "output best, last, avg weights ('VOID' to throw away)")
-    ("rescale",           po::value<bool>()->zero_tokens(),                              "rescale weight vector after each input")
-    ("l1_reg",            po::value<string>()->default_value("none"),      "apply l1 regularization as in 'Tsuroka et al' (2010)")
-    ("l1_reg_strength",   po::value<weight_t>(),                                                     "l1 regularization strength")
-    ("fselect",           po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO
-    ("approx_bleu_d",     po::value<score_t>()->default_value(0.9),                                   "discount for approx. BLEU")
-    ("scale_bleu_diff",   po::value<bool>()->zero_tokens(),                      "learning rate <- bleu diff of a misranked pair")
-    ("loss_margin",       po::value<weight_t>()->default_value(0.),  "update if no error in pref pair but model scores this near")
-    ("max_pairs",         po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.")
-#ifdef DTRAIN_LOCAL
-    ("refs,r",            po::value<string>(),                                                         "references in local mode")
-#endif
-    ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
-  po::options_description cl("Command Line Options");
-  cl.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");
-  cl.add(ini);
-  po::store(parse_command_line(argc, argv, cl), *cfg);
-  if (cfg->count("config")) {
-    ifstream ini_f((*cfg)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(ini_f, ini), *cfg);
-  }
-  po::notify(*cfg);
-  if (!cfg->count("decoder_config")) {
-    cerr << cl << endl;
-    return false;
-  }
-  if (cfg->count("hstreaming") && (*cfg)["output"].as<string>() != "-") {
-    cerr << "When using 'hstreaming' the 'output' param should be '-'." << endl;
-    return false;
-  }
-#ifdef DTRAIN_LOCAL
-  if ((*cfg)["input"].as<string>() == "-") {
-    cerr << "Can't use stdin as input with this binary. Recompile without DTRAIN_LOCAL" << endl;
-    return false;
-  }
-#endif
-  if ((*cfg)["sample_from"].as<string>() != "kbest"
-       && (*cfg)["sample_from"].as<string>() != "forest") {
-    cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
-    return false;
-  }
-  if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq" &&
-        (*cfg)["filter"].as<string>() != "not") {
-    cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl;
-    return false;
-  }
-  if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" &&
-        (*cfg)["pair_sampling"].as<string>() != "PRO") {
-    cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
-    return false;
-  }
-  if(cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") {
-    cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl;
-  }
-  if((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) {
-    cerr << "hi_lo must lie in [0.01, 0.5]" << endl;
-    return false;
-  }
-  if ((*cfg)["pair_threshold"].as<score_t>() < 0) {
-    cerr << "The threshold must be >= 0!" << endl;
-    return false;
-  }
-  if ((*cfg)["select_weights"].as<string>() != "last" && (*cfg)["select_weights"].as<string>() != "best" &&
-        (*cfg)["select_weights"].as<string>() != "avg" && (*cfg)["select_weights"].as<string>() != "VOID") {
-    cerr << "Wrong 'select_weights' param: '" << (*cfg)["select_weights"].as<string>() << "', use 'last' or 'best'." << endl;
-    return false;
-  }
-  return true;
-}
-
-int
-main(int argc, char** argv)
-{
-  // handle most parameters
-  po::variables_map cfg;
-  if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
-  bool quiet = false;
-  if (cfg.count("quiet")) quiet = true;
-  bool verbose = false;
-  if (cfg.count("verbose")) verbose = true;
-  bool noup = false;
-  if (cfg.count("noup")) noup = true;
-  bool hstreaming = false;
-  string task_id;
-  if (cfg.count("hstreaming")) {
-    hstreaming = true;
-    quiet = true;
-    task_id = cfg["hstreaming"].as<string>();
-    cerr.precision(17);
-  }
-  bool rescale = false;
-  if (cfg.count("rescale")) rescale = true;
-  HSReporter rep(task_id);
-  bool keep = false;
-  if (cfg.count("keep")) keep = true;
-
-  const unsigned k = cfg["k"].as<unsigned>();
-  const unsigned N = cfg["N"].as<unsigned>();
-  const unsigned T = cfg["epochs"].as<unsigned>();
-  const unsigned stop_after = cfg["stop_after"].as<unsigned>();
-  const string filter_type = cfg["filter"].as<string>();
-  const string sample_from = cfg["sample_from"].as<string>();
-  const string pair_sampling = cfg["pair_sampling"].as<string>();
-  const score_t pair_threshold = cfg["pair_threshold"].as<score_t>();
-  const string select_weights = cfg["select_weights"].as<string>();
-  const float hi_lo = cfg["hi_lo"].as<float>();
-  const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>();
-  const unsigned max_pairs = cfg["max_pairs"].as<unsigned>();
-  weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
-  if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max();
-  bool scale_bleu_diff = false;
-  if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
-  bool average = false;
-  if (select_weights == "avg")
-    average = true;
-  vector<string> print_weights;
-  if (cfg.count("print_weights"))
-    boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
-
-  // setup decoder
-  register_feature_functions();
-  SetSilent(true);
-  ReadFile ini_rf(cfg["decoder_config"].as<string>());
-  if (!quiet)
-    cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
-  Decoder decoder(ini_rf.stream());
-
-  // scoring metric/scorer
-  string scorer_str = cfg["scorer"].as<string>();
-  LocalScorer* scorer;
-  if (scorer_str == "bleu") {
-    scorer = dynamic_cast<BleuScorer*>(new BleuScorer);
-  } else if (scorer_str == "stupid_bleu") {
-    scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer);
-  } else if (scorer_str == "smooth_bleu") {
-    scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer);
-  } else if (scorer_str == "sum_bleu") {
-    scorer = dynamic_cast<SumBleuScorer*>(new SumBleuScorer);
-  } else if (scorer_str == "sumexp_bleu") {
-    scorer = dynamic_cast<SumExpBleuScorer*>(new SumExpBleuScorer);
-  } else if (scorer_str == "sumwhatever_bleu") {
-    scorer = dynamic_cast<SumWhateverBleuScorer*>(new SumWhateverBleuScorer);
-  } else if (scorer_str == "approx_bleu") {
-    scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d));
-  } else if (scorer_str == "lc_bleu") {
-    scorer = dynamic_cast<LinearBleuScorer*>(new LinearBleuScorer(N));
-  } else {
-    cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
-    exit(1);
-  }
-  vector<score_t> bleu_weights;
-  scorer->Init(N, bleu_weights);
-
-  // setup decoder observer
-  MT19937 rng; // random number generator, only for forest sampling
-  HypSampler* observer;
-  if (sample_from == "kbest")
-    observer = dynamic_cast<KBestGetter*>(new KBestGetter(k, filter_type));
-  else
-    observer = dynamic_cast<KSampler*>(new KSampler(k, &rng));
-  observer->SetScorer(scorer);
-
-  // init weights
-  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
-  SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
-  if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights);
-  Weights::InitSparseVector(dense_weights, &lambdas);
-
-  // meta params for perceptron, SVM
-  weight_t eta = cfg["learning_rate"].as<weight_t>();
-  weight_t gamma = cfg["gamma"].as<weight_t>();
-
-  // l1 regularization
-  bool l1naive = false;
-  bool l1clip = false;
-  bool l1cumul = false;
-  weight_t l1_reg = 0;
-  if (cfg["l1_reg"].as<string>() != "none") {
-    string s = cfg["l1_reg"].as<string>();
-    if (s == "naive") l1naive = true;
-    else if (s == "clip") l1clip = true;
-    else if (s == "cumul") l1cumul = true;
-    l1_reg = cfg["l1_reg_strength"].as<weight_t>();
-  }
-
-  // output
-  string output_fn = cfg["output"].as<string>();
-  // input
-  string input_fn = cfg["input"].as<string>();
-  ReadFile input(input_fn);
-  // buffer input for t > 0
-  vector<string> src_str_buf;          // source strings (decoder takes only strings)
-  vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
-  // where temp files go
-  string tmp_path = cfg["tmp"].as<string>();
-#ifdef DTRAIN_LOCAL
-  string refs_fn = cfg["refs"].as<string>();
-  ReadFile refs(refs_fn);
-#else
-  string grammar_buf_fn = gettmpf(tmp_path, "dtrain-grammars");
-  ogzstream grammar_buf_out;
-  grammar_buf_out.open(grammar_buf_fn.c_str());
-#endif
-
-  unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size
-  vector<pair<score_t, score_t> > all_scores;
-  score_t max_score = 0.;
-  unsigned best_it = 0;
-  float overall_time = 0.;
-
-  // output cfg
-  if (!quiet) {
-    cerr << _p5;
-    cerr << endl << "dtrain" << endl << "Parameters:" << endl;
-    cerr << setw(25) << "k " << k << endl;
-    cerr << setw(25) << "N " << N << endl;
-    cerr << setw(25) << "T " << T << endl;
-    cerr << setw(25) << "scorer '" << scorer_str << "'" << endl;
-    if (scorer_str == "approx_bleu")
-      cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl;
-    cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
-    if (sample_from == "kbest")
-      cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
-    if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl;
-    else cerr << setw(25) << "learning rate " << "bleu diff" << endl;
-    cerr << setw(25) << "gamma " << gamma << endl;
-    cerr << setw(25) << "loss margin " << loss_margin << endl;
-    cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
-    if (pair_sampling == "XYX")
-      cerr << setw(25) << "hi lo " << hi_lo << endl;
-    cerr << setw(25) << "pair threshold " << pair_threshold << endl;
-    cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl;
-    if (cfg.count("l1_reg"))
-      cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
-    if (rescale)
-      cerr << setw(25) << "rescale " << rescale << endl;
-    cerr << setw(25) << "max pairs " << max_pairs << endl;
-    cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
-    cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
-#ifdef DTRAIN_LOCAL
-    cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
-#endif
-    cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
-    if (cfg.count("input_weights"))
-      cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
-    if (stop_after > 0)
-      cerr << setw(25) << "stop_after " << stop_after << endl;
-    if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
-  }
-
-
-  for (unsigned t = 0; t < T; t++) // T epochs
-  {
-
-  if (hstreaming) cerr << "reporter:status:Iteration #" << t+1 << " of " << T << endl;
-
-  time_t start, end;
-  time(&start);
-#ifndef DTRAIN_LOCAL
-  igzstream grammar_buf_in;
-  if (t > 0) grammar_buf_in.open(grammar_buf_fn.c_str());
-#endif
-  score_t score_sum = 0.;
-  score_t model_sum(0);
-  unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0;
-  if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
-
-  while(true)
-  {
-
-    string in;
-    bool next = false, stop = false; // next iteration or premature stop
-    if (t == 0) {
-      if(!getline(*input, in)) next = true;
-    } else {
-      if (ii == in_sz) next = true; // stop if we reach the end of our input
-    }
-    // stop after X sentences (but still go on for those)
-    if (stop_after > 0 && stop_after == ii && !next) stop = true;
-
-    // produce some pretty output
-    if (!quiet && !verbose) {
-      if (ii == 0) cerr << " ";
-      if ((ii+1) % (DTRAIN_DOTS) == 0) {
-        cerr << ".";
-        cerr.flush();
-      }
-      if ((ii+1) % (20*DTRAIN_DOTS) == 0) {
-        cerr << " " << ii+1 << endl;
-        if (!next && !stop) cerr << " ";
-      }
-      if (stop) {
-        if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
-        cerr << "Stopping after " << stop_after << " input sentences." << endl;
-      } else {
-        if (next) {
-          if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
-        }
-      }
-    }
-
-    // next iteration
-    if (next || stop) break;
-
-    // weights
-    lambdas.init_vector(&dense_weights);
-
-    // getting input
-    vector<WordID> ref_ids; // reference as vector<WordID>
-#ifndef DTRAIN_LOCAL
-    vector<string> in_split; // input: sid\tsrc\tref\tpsg
-    if (t == 0) {
-      // handling input
-      split_in(in, in_split);
-      if (hstreaming && ii == 0) cerr << "reporter:counter:" << task_id << ",First ID," << in_split[0] << endl;
-      // getting reference
-      vector<string> ref_tok;
-      boost::split(ref_tok, in_split[2], boost::is_any_of(" "));
-      register_and_convert(ref_tok, ref_ids);
-      ref_ids_buf.push_back(ref_ids);
-      // process and set grammar
-      bool broken_grammar = true; // ignore broken grammars
-      for (string::iterator it = in.begin(); it != in.end(); it++) {
-        if (!isspace(*it)) {
-          broken_grammar = false;
-          break;
-        }
-      }
-      if (broken_grammar) {
-        cerr << "Broken grammar for " << ii+1 << "! Ignoring this input." << endl;
-        continue;
-      }
-      boost::replace_all(in, "\t", "\n");
-      in += "\n";
-      grammar_buf_out << in << DTRAIN_GRAMMAR_DELIM << " " << in_split[0] << endl;
-      decoder.AddSupplementalGrammarFromString(in);
-      src_str_buf.push_back(in_split[1]);
-      // decode
-      observer->SetRef(ref_ids);
-      decoder.Decode(in_split[1], observer);
-    } else {
-      // get buffered grammar
-      string grammar_str;
-      while (true) {
-        string rule;
-        getline(grammar_buf_in, rule);
-        if (boost::starts_with(rule, DTRAIN_GRAMMAR_DELIM)) break;
-        grammar_str += rule + "\n";
-      }
-      decoder.AddSupplementalGrammarFromString(grammar_str);
-      // decode
-      observer->SetRef(ref_ids_buf[ii]);
-      decoder.Decode(src_str_buf[ii], observer);
-    }
-#else
-    if (t == 0) {
-      string r_;
-      getline(*refs, r_);
-      vector<string> ref_tok;
-      boost::split(ref_tok, r_, boost::is_any_of(" "));
-      register_and_convert(ref_tok, ref_ids);
-      ref_ids_buf.push_back(ref_ids);
-      src_str_buf.push_back(in);
-    } else {
-      ref_ids = ref_ids_buf[ii];
-    }
-    observer->SetRef(ref_ids);
-    if (t == 0)
-      decoder.Decode(in, observer);
-    else
-      decoder.Decode(src_str_buf[ii], observer);
-#endif
-
-    // get (scored) samples
-    vector<ScoredHyp>* samples = observer->GetSamples();
-
-    if (verbose) {
-      cerr << "--- ref for " << ii << ": ";
-      if (t > 0) printWordIDVec(ref_ids_buf[ii]);
-      else printWordIDVec(ref_ids);
-      cerr << endl;
-      for (unsigned u = 0; u < samples->size(); u++) {
-        cerr << _p2 << _np << "[" << u << ". '";
-        printWordIDVec((*samples)[u].w);
-        cerr << "'" << endl;
-        cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl;
-        cerr << "F{" << (*samples)[u].f << "} ]" << endl << endl;
-      }
-    }
-
-    score_sum += (*samples)[0].score; // stats for 1best
-    model_sum += (*samples)[0].model;
-
-    f_count += observer->get_f_count();
-    list_sz += observer->get_sz();
-
-    // weight updates
-    if (!noup) {
-      // get pairs
-      vector<pair<ScoredHyp,ScoredHyp> > pairs;
-      if (pair_sampling == "all")
-        all_pairs(samples, pairs, pair_threshold, max_pairs);
-      if (pair_sampling == "XYX")
-        partXYX(samples, pairs, pair_threshold, max_pairs, hi_lo);
-      if (pair_sampling == "PRO")
-        PROsampling(samples, pairs, pair_threshold, max_pairs);
-      npairs += pairs.size();
-
-      for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
-           it != pairs.end(); it++) {
-#ifdef DTRAIN_FASTER_PERCEPTRON
-        bool rank_error = true; // pair sampling already did this for us
-        rank_errors++;
-        score_t margin = std::numeric_limits<float>::max();
-#else
-        bool rank_error = it->first.model <= it->second.model;
-        if (rank_error) rank_errors++;
-        score_t margin = fabs(fabs(it->first.model) - fabs(it->second.model));
-        if (!rank_error && margin < loss_margin) margin_violations++;
-#endif
-        if (scale_bleu_diff) eta = it->first.score - it->second.score;
-        if (rank_error || margin < loss_margin) {
-          SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
-          lambdas.plus_eq_v_times_s(diff_vec, eta);
-          if (gamma)
-            lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
-        }
-      }
-
-      // l1 regularization
-      if (l1naive) {
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          weight_t v = lambdas.get(d);
-          lambdas.set_value(d, v - sign(v) * l1_reg);
-        }
-      } else if (l1clip) {
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          if (lambdas.nonzero(d)) {
-            weight_t v = lambdas.get(d);
-            if (v > 0) {
-              lambdas.set_value(d, max(0., v - l1_reg));
-            } else {
-              lambdas.set_value(d, min(0., v + l1_reg));
-            }
-          }
-        }
-      } else if (l1cumul) {
-        weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          if (lambdas.nonzero(d)) {
-            weight_t v = lambdas.get(d);
-            weight_t penalty = 0;
-            if (v > 0) {
-              penalty = max(0., v-(acc_penalty + cumulative_penalties.get(d)));
-            } else {
-              penalty = min(0., v+(acc_penalty - cumulative_penalties.get(d)));
-            }
-            lambdas.set_value(d, penalty);
-            cumulative_penalties.set_value(d, cumulative_penalties.get(d)+penalty);
-          }
-        }
-      }
-
-    }
-
-    if (rescale) lambdas /= lambdas.l2norm();
-
-    ++ii;
-
-    if (hstreaming) {
-      rep.update_counter("Seen #"+boost::lexical_cast<string>(t+1), 1u);
-      rep.update_counter("Seen", 1u);
-    }
-
-  } // input loop
-
-  if (average) w_average += lambdas;
-
-  if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
-
-  if (t == 0) {
-    in_sz = ii; // remember size of input (# lines)
-    if (hstreaming) {
-      rep.update_counter("|Input|", ii);
-      rep.update_gcounter("|Input|", ii);
-      rep.update_gcounter("Shards", 1u);
-    }
-  }
-
-#ifndef DTRAIN_LOCAL
-  if (t == 0) {
-    grammar_buf_out.close();
-  } else {
-    grammar_buf_in.close();
-  }
-#endif
-
-  // print some stats
-  score_t score_avg = score_sum/(score_t)in_sz;
-  score_t model_avg = model_sum/(score_t)in_sz;
-  score_t score_diff, model_diff;
-  if (t > 0) {
-    score_diff = score_avg - all_scores[t-1].first;
-    model_diff = model_avg - all_scores[t-1].second;
-  } else {
-    score_diff = score_avg;
-    model_diff = model_avg;
-  }
-
-  unsigned nonz = 0;
-  if (!quiet || hstreaming) nonz = (unsigned)lambdas.num_nonzero();
-
-  if (!quiet) {
-    cerr << _p5 << _p << "WEIGHTS" << endl;
-    for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) {
-      cerr << setw(18) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl;
-    }
-    cerr << "        ---" << endl;
-    cerr << _np << "       1best avg score: " << score_avg;
-    cerr << _p << " (" << score_diff << ")" << endl;
-    cerr << _np << " 1best avg model score: " << model_avg;
-    cerr << _p << " (" << model_diff << ")" << endl;
-    cerr << "           avg # pairs: ";
-    cerr << _np << npairs/(float)in_sz << endl;
-    cerr << "        avg # rank err: ";
-    cerr << rank_errors/(float)in_sz << endl;
-#ifndef DTRAIN_FASTER_PERCEPTRON
-    cerr << "     avg # margin viol: ";
-    cerr << margin_violations/(float)in_sz << endl;
-#endif
-    cerr << "    non0 feature count: " <<  nonz << endl;
-    cerr << "           avg list sz: " << list_sz/(float)in_sz << endl;
-    cerr << "           avg f count: " << f_count/(float)list_sz << endl;
-  }
-
-  if (hstreaming) {
-    rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*DTRAIN_SCALE));
-    rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*DTRAIN_SCALE));
-    rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*DTRAIN_SCALE));
-    rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*DTRAIN_SCALE));
-    rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*DTRAIN_SCALE));
-    rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
-    rep.update_gcounter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
-  }
-
-  pair<score_t,score_t> remember;
-  remember.first = score_avg;
-  remember.second = model_avg;
-  all_scores.push_back(remember);
-  if (score_avg > max_score) {
-    max_score = score_avg;
-    best_it = t;
-  }
-  time (&end);
-  float time_diff = difftime(end, start);
-  overall_time += time_diff;
-  if (!quiet) {
-    cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
-    cerr << time_diff/in_sz << " s/S)" << endl;
-  }
-  if (t+1 != T && !quiet) cerr << endl;
-
-  if (noup) break;
-
-  // write weights to file
-  if (select_weights == "best" || keep) {
-    lambdas.init_vector(&dense_weights);
-    string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
-    Weights::WriteToFile(w_fn, dense_weights, true);
-  }
-
-  } // outer loop
-
-  if (average) w_average /= (weight_t)T;
-
-#ifndef DTRAIN_LOCAL
-  unlink(grammar_buf_fn.c_str());
-#endif
-
-  if (!noup) {
-    if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl;
-    if (select_weights == "last" || average) { // last, average
-      WriteFile of(output_fn); // works with '-'
-      ostream& o = *of.stream();
-      o.precision(17);
-      o << _np;
-      if (average) {
-        for (SparseVector<weight_t>::iterator it = w_average.begin(); it != w_average.end(); ++it) {
-	      if (it->second == 0) continue;
-          o << FD::Convert(it->first) << '\t' << it->second << endl;
-        }
-      } else {
-        for (SparseVector<weight_t>::iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
-	      if (it->second == 0) continue;
-          o << FD::Convert(it->first) << '\t' << it->second << endl;
-        }
-      }
-    } else if (select_weights == "VOID") { // do nothing with the weights
-    } else { // best
-      if (output_fn != "-") {
-        CopyFile("weights."+boost::lexical_cast<string>(best_it)+".gz", output_fn);
-      } else {
-        ReadFile bestw("weights."+boost::lexical_cast<string>(best_it)+".gz");
-        string o;
-        cout.precision(17);
-        cout << _np;
-        while(getline(*bestw, o)) cout << o << endl;
-      }
-      if (!keep) {
-        for (unsigned i = 0; i < T; i++) {
-          string s = "weights." + boost::lexical_cast<string>(i) + ".gz";
-          unlink(s.c_str());
-        }
-      }
-    }
-    if (output_fn == "-" && hstreaming) cout << "__SHARD_COUNT__\t1" << endl;
-    if (!quiet) cerr << "done" << endl;
-  }
-
-  if (!quiet) {
-    cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
-    cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
-    cerr << "This took " << overall_time/60. << " min." << endl;
-  }
-}
-