Merge branch 'master' of https://github.com/redpony/cdec

1 files changed, 50 insertions, 154 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 18286668..149f87d4 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -6,15 +6,14 @@ 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")
+    ("input",             po::value<string>()->default_value("-"),                                             "input file (src)")
+    ("refs,r",            po::value<string>(),                                                                       "references")
     ("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'")
@@ -28,16 +27,13 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("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",            po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010) UNTESTED")
     ("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()
@@ -55,16 +51,6 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     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;
@@ -111,17 +97,8 @@ main(int argc, char** argv)
   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;
 
@@ -148,6 +125,7 @@ main(int argc, char** argv)
   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);
@@ -163,6 +141,8 @@ main(int argc, char** argv)
     scorer = dynamic_cast<BleuScorer*>(new BleuScorer);
   } else if (scorer_str == "stupid_bleu") {
     scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer);
+  } else if (scorer_str == "fixed_stupid_bleu") {
+    scorer = dynamic_cast<FixedStupidBleuScorer*>(new FixedStupidBleuScorer);
   } else if (scorer_str == "smooth_bleu") {
     scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer);
   } else if (scorer_str == "sum_bleu") {
@@ -201,6 +181,11 @@ main(int argc, char** argv)
   weight_t eta = cfg["learning_rate"].as<weight_t>();
   weight_t gamma = cfg["gamma"].as<weight_t>();
 
+  // faster perceptron: consider only misranked pairs, see
+  // DO NOT ENABLE  WITH SVM (gamma > 0) OR loss_margin!
+  bool faster_perceptron = false;
+  if (gamma==0 && loss_margin==0) faster_perceptron = true;
+
   // l1 regularization
   bool l1naive = false;
   bool l1clip = false;
@@ -222,16 +207,8 @@ main(int argc, char** argv)
   // 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;
@@ -246,7 +223,7 @@ main(int argc, char** argv)
     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;
+    cerr << setw(26) << "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;
@@ -256,6 +233,7 @@ main(int argc, char** argv)
     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) << "faster perceptron " << faster_perceptron << endl;
     cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
     if (pair_sampling == "XYX")
       cerr << setw(25) << "hi lo " << hi_lo << endl;
@@ -268,9 +246,7 @@ main(int argc, char** argv)
     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;
@@ -283,14 +259,8 @@ main(int argc, char** argv)
   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;
@@ -338,52 +308,6 @@ main(int argc, char** argv)
 
     // 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_);
@@ -400,7 +324,6 @@ main(int argc, char** argv)
       decoder.Decode(in, observer);
     else
       decoder.Decode(src_str_buf[ii], observer);
-#endif
 
     // get (scored) samples
     vector<ScoredHyp>* samples = observer->GetSamples();
@@ -430,25 +353,26 @@ main(int argc, char** argv)
       // get pairs
       vector<pair<ScoredHyp,ScoredHyp> > pairs;
       if (pair_sampling == "all")
-        all_pairs(samples, pairs, pair_threshold, max_pairs);
+        all_pairs(samples, pairs, pair_threshold, max_pairs, faster_perceptron);
       if (pair_sampling == "XYX")
-        partXYX(samples, pairs, pair_threshold, max_pairs, hi_lo);
+        partXYX(samples, pairs, pair_threshold, max_pairs, faster_perceptron, 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;
+        bool rank_error;
+        score_t margin;
+        if (faster_perceptron) { // we only have considering misranked pairs
+          rank_error = true; // pair sampling already did this for us
+          margin = std::numeric_limits<float>::max();
+        } else {
+          rank_error = it->first.model <= it->second.model;
+          margin = fabs(fabs(it->first.model) - fabs(it->second.model));
+          if (!rank_error && margin < loss_margin) margin_violations++;
+        }
         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;
@@ -459,35 +383,40 @@ main(int argc, char** argv)
       }
 
       // l1 regularization
+      // please note that this penalizes _all_ weights
+      // (contrary to only the ones changed by the last update)
+      // after a _sentence_ (not after each example/pair)
       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);
+        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        for (; it != lambdas.end(); ++it) {
+          it->second -= sign(it->second) * l1_reg;
         }
       } else if (l1clip) {
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          if (lambdas.nonzero(d)) {
-            weight_t v = lambdas.get(d);
+        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        for (; it != lambdas.end(); ++it) {
+          if (it->second != 0) {
+            weight_t v = it->second;
             if (v > 0) {
-              lambdas.set_value(d, max(0., v - l1_reg));
+              it->second = max(0., v - l1_reg);
             } else {
-              lambdas.set_value(d, min(0., v + l1_reg));
+              it->second = 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;
+        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        for (; it != lambdas.end(); ++it) {
+          if (it->second != 0) {
+            weight_t v = it->second;
+            weight_t penalized = 0.;
             if (v > 0) {
-              penalty = max(0., v-(acc_penalty + cumulative_penalties.get(d)));
+              penalized = max(0., v-(acc_penalty + cumulative_penalties.get(it->first)));
             } else {
-              penalty = min(0., v+(acc_penalty - cumulative_penalties.get(d)));
+              penalized = min(0., v+(acc_penalty - cumulative_penalties.get(it->first)));
             }
-            lambdas.set_value(d, penalty);
-            cumulative_penalties.set_value(d, cumulative_penalties.get(d)+penalty);
+            it->second = penalized;
+            cumulative_penalties.set_value(it->first, cumulative_penalties.get(it->first)+penalized);
           }
         }
       }
@@ -498,11 +427,6 @@ main(int argc, char** argv)
 
     ++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;
@@ -511,21 +435,8 @@ main(int argc, char** argv)
 
   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;
@@ -539,7 +450,7 @@ main(int argc, char** argv)
   }
 
   unsigned nonz = 0;
-  if (!quiet || hstreaming) nonz = (unsigned)lambdas.num_nonzero();
+  if (!quiet) nonz = (unsigned)lambdas.num_nonzero();
 
   if (!quiet) {
     cerr << _p5 << _p << "WEIGHTS" << endl;
@@ -552,28 +463,18 @@ main(int argc, char** argv)
     cerr << _np << " 1best avg model score: " << model_avg;
     cerr << _p << " (" << model_diff << ")" << endl;
     cerr << "           avg # pairs: ";
-    cerr << _np << npairs/(float)in_sz << endl;
+    cerr << _np << npairs/(float)in_sz;
+    if (faster_perceptron) cerr << " (meaningless)";
+    cerr << 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;
@@ -604,10 +505,6 @@ main(int argc, char** argv)
 
   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
@@ -644,7 +541,6 @@ main(int argc, char** argv)
         }
       }
     }
-    if (output_fn == "-" && hstreaming) cout << "__SHARD_COUNT__\t1" << endl;
     if (!quiet) cerr << "done" << endl;
   }