Merge pull request #27 from pks/master

Tidying (soft) syntax features; loo for C++ extractor; updates for dtrain 

1 files changed, 160 insertions, 41 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 0ee2f124..0a27a068 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -12,8 +12,9 @@ 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 (src)")
+    ("input",             po::value<string>(),                                                                 "input file (src)")
     ("refs,r",            po::value<string>(),                                                                       "references")
+    ("bitext,b",          po::value<string>(),                                                            "bitext: 'src ||| tgt'")
     ("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")
@@ -40,6 +41,10 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("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.")
+    ("pclr",              po::value<string>()->default_value("no"),         "use a (simple|adagrad) per-coordinate learning rate")
+    ("batch",             po::value<bool>()->zero_tokens(),                                               "do batch optimization")
+    ("repeat",            po::value<unsigned>()->default_value(1),          "repeat optimization over kbest list this number of times")
+    //("test-k-best",       po::value<bool>()->zero_tokens(),                       "check if optimization works (use repeat >= 2)")
     ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
   po::options_description cl("Command Line Options");
   cl.add_options()
@@ -72,13 +77,17 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
     return false;
   }
-  if(cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") {
+  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) {
+  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->count("input")>0 || cfg->count("refs")>0) && cfg->count("bitext")>0) {
+    cerr << "Provide 'input' and 'refs' or 'bitext', not both." << endl;
+    return false;
+  }
   if ((*cfg)["pair_threshold"].as<score_t>() < 0) {
     cerr << "The threshold must be >= 0!" << endl;
     return false;
@@ -120,10 +129,16 @@ main(int argc, char** argv)
   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>();
+  int repeat = cfg["repeat"].as<unsigned>();
+  //bool test_k_best = false;
+  //if (cfg.count("test-k-best")) test_k_best = true;
   weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
+  bool batch = false;
+  if (cfg.count("batch")) batch = true;
   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;
+  const string pclr = cfg["pclr"].as<string>();
   bool average = false;
   if (select_weights == "avg")
     average = true;
@@ -131,7 +146,6 @@ 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);
@@ -178,17 +192,16 @@ main(int argc, char** argv)
   observer->SetScorer(scorer);
 
   // init weights
-  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+  vector<weight_t>& decoder_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);
+  if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &decoder_weights);
+  Weights::InitSparseVector(decoder_weights, &lambdas);
 
   // meta params for perceptron, SVM
   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;
 
@@ -208,13 +221,24 @@ main(int argc, char** argv)
   // output
   string output_fn = cfg["output"].as<string>();
   // input
-  string input_fn = cfg["input"].as<string>();
+  bool read_bitext = false;
+  string input_fn;
+  if (cfg.count("bitext")) {
+    read_bitext = true;
+    input_fn = cfg["bitext"].as<string>();
+  } else {
+    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
-  string refs_fn = cfg["refs"].as<string>();
-  ReadFile refs(refs_fn);
+  ReadFile refs;
+  string refs_fn;
+  if (!read_bitext) {
+    refs_fn = cfg["refs"].as<string>();
+    refs.Init(refs_fn);
+  }
 
   unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size
   vector<pair<score_t, score_t> > all_scores;
@@ -229,6 +253,7 @@ 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) << "batch " << batch << endl;
     cerr << setw(26) << "scorer '" << scorer_str << "'" << endl;
     if (scorer_str == "approx_bleu")
       cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl;
@@ -249,10 +274,14 @@ main(int argc, char** argv)
       cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
     if (rescale)
       cerr << setw(25) << "rescale " << rescale << endl;
+    cerr << setw(25) << "pclr " << pclr << endl;
     cerr << setw(25) << "max pairs " << max_pairs << endl;
+    cerr << setw(25) << "repeat " << repeat << endl;
+    //cerr << setw(25) << "test k-best " << test_k_best << endl;
     cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
     cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
-    cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
+    if (!read_bitext)
+      cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
     cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
     if (cfg.count("input_weights"))
       cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
@@ -261,6 +290,11 @@ main(int argc, char** argv)
     if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
   }
 
+  // pclr
+  SparseVector<weight_t> learning_rates;
+  // batch
+  SparseVector<weight_t> batch_updates;
+  score_t batch_loss;
 
   for (unsigned t = 0; t < T; t++) // T epochs
   {
@@ -269,16 +303,24 @@ main(int argc, char** argv)
   time(&start);
   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;
+  unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0, kbest_loss_improve = 0;
+  batch_loss = 0.;
   if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
 
   while(true)
   {
 
     string in;
+    string ref;
     bool next = false, stop = false; // next iteration or premature stop
     if (t == 0) {
       if(!getline(*input, in)) next = true;
+      if(read_bitext) {
+        vector<string> strs;
+        boost::algorithm::split_regex(strs, in, boost::regex(" \\|\\|\\| "));
+        in = strs[0];
+        ref = strs[1];
+      }
     } else {
       if (ii == in_sz) next = true; // stop if we reach the end of our input
     }
@@ -310,15 +352,16 @@ main(int argc, char** argv)
     if (next || stop) break;
 
     // weights
-    lambdas.init_vector(&dense_weights);
+    lambdas.init_vector(&decoder_weights);
 
     // getting input
     vector<WordID> ref_ids; // reference as vector<WordID>
     if (t == 0) {
-      string r_;
-      getline(*refs, r_);
+      if (!read_bitext) {
+        getline(*refs, ref);
+      }
       vector<string> ref_tok;
-      boost::split(ref_tok, r_, boost::is_any_of(" "));
+      boost::split(ref_tok, ref, boost::is_any_of(" "));
       register_and_convert(ref_tok, ref_ids);
       ref_ids_buf.push_back(ref_ids);
       src_str_buf.push_back(in);
@@ -348,8 +391,10 @@ main(int argc, char** argv)
       }
     }
 
-    score_sum += (*samples)[0].score; // stats for 1best
-    model_sum += (*samples)[0].model;
+    if (repeat == 1) {
+      score_sum += (*samples)[0].score; // stats for 1best
+      model_sum += (*samples)[0].model;
+    }
 
     f_count += observer->get_f_count();
     list_sz += observer->get_sz();
@@ -364,30 +409,74 @@ main(int argc, char** argv)
         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();
+      int cur_npairs = pairs.size();
+      npairs += cur_npairs;
+
+      score_t kbest_loss_first, kbest_loss_last = 0.0;
 
-      SparseVector<weight_t> lambdas_copy;
+      for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
+           it != pairs.end(); it++) {
+        score_t model_diff = it->first.model - it->second.model;
+        kbest_loss_first += max(0.0, -1.0 * model_diff);
+      }
+
+      for (int ki=0; ki < repeat; ki++) {
+
+      score_t kbest_loss = 0.0; // test-k-best
+      SparseVector<weight_t> lambdas_copy; // for l1 regularization
+      SparseVector<weight_t> sum_up; // for pclr
       if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas;
 
       for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
            it != pairs.end(); it++) {
-        bool rank_error;
+        score_t model_diff = it->first.model - it->second.model;
+        if (repeat > 1) {
+          model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f);
+          kbest_loss += max(0.0, -1.0 * model_diff);
+        }
+        bool rank_error = false;
         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(it->first.model - it->second.model);
+          rank_error = model_diff<=0.0;
+          margin = fabs(model_diff);
           if (!rank_error && margin < loss_margin) margin_violations++;
         }
-        if (rank_error) rank_errors++;
+        if (rank_error && ki==1) rank_errors++;
         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));
+          if (batch) {
+            batch_loss += max(0., -1.0*model_diff);
+            batch_updates += diff_vec;
+            continue;
+          }
+          if (pclr != "no") {
+            sum_up += diff_vec;
+          } else {
+            lambdas.plus_eq_v_times_s(diff_vec, eta);
+            if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./cur_npairs));
+          }
+        }
+      }
+
+      // per-coordinate learning rate
+      if (pclr != "no") {
+        SparseVector<weight_t>::iterator it = sum_up.begin();
+        for (; it != sum_up.end(); ++it) {
+          if (pclr == "simple") {
+           lambdas[it->first] += it->second / max(1.0, learning_rates[it->first]);
+           learning_rates[it->first]++;
+          } else if (pclr == "adagrad") {
+            if (learning_rates[it->first] == 0) {
+             lambdas[it->first] +=  it->second * eta;
+            } else {
+             lambdas[it->first] +=  it->second * eta * learning_rates[it->first];
+            }
+            learning_rates[it->first] += pow(it->second, 2.0);
+          }
         }
       }
 
@@ -395,14 +484,16 @@ main(int argc, char** argv)
       // please note that this regularizations happen
       // after a _sentence_ -- not after each example/pair!
       if (l1naive) {
-        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        SparseVector<weight_t>::iterator it = lambdas.begin();
         for (; it != lambdas.end(); ++it) {
           if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
+              it->second *= max(0.0000001, eta/(eta+learning_rates[it->first])); // FIXME
+              learning_rates[it->first]++;
             it->second -= sign(it->second) * l1_reg;
           }
         }
       } else if (l1clip) {
-        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        SparseVector<weight_t>::iterator it = lambdas.begin();
         for (; it != lambdas.end(); ++it) {
           if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
             if (it->second != 0) {
@@ -417,7 +508,7 @@ main(int argc, char** argv)
         }
       } else if (l1cumul) {
         weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
-        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        SparseVector<weight_t>::iterator it = lambdas.begin();
         for (; it != lambdas.end(); ++it) {
           if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
             if (it->second != 0) {
@@ -435,7 +526,28 @@ main(int argc, char** argv)
         }
       }
 
-    }
+      if (ki==repeat-1) { // done
+        kbest_loss_last = kbest_loss;
+        if (repeat > 1) {
+          score_t best_score = -1.;
+          score_t best_model = -std::numeric_limits<score_t>::max();
+          unsigned best_idx;
+          for (unsigned i=0; i < samples->size(); i++) {
+            score_t s = lambdas.dot((*samples)[i].f);
+            if (s > best_model) {
+              best_idx = i;
+              best_model = s;
+            }
+          }
+          score_sum += (*samples)[best_idx].score;
+          model_sum += best_model;
+        }
+      }
+    } // repeat
+
+    if ((kbest_loss_first - kbest_loss_last) >= 0) kbest_loss_improve++;
+
+    } // noup
 
     if (rescale) lambdas /= lambdas.l2norm();
 
@@ -443,14 +555,19 @@ main(int argc, char** argv)
 
   } // input loop
 
-  if (average) w_average += lambdas;
+  if (t == 0) in_sz = ii; // remember size of input (# lines)
 
-  if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
 
-  if (t == 0) {
-    in_sz = ii; // remember size of input (# lines)
+  if (batch) {
+    lambdas.plus_eq_v_times_s(batch_updates, eta);
+    if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
+    batch_updates.clear();
   }
 
+  if (average) w_average += lambdas;
+
+  if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
+
   // print some stats
   score_t score_avg = score_sum/(score_t)in_sz;
   score_t model_avg = model_sum/(score_t)in_sz;
@@ -477,13 +594,15 @@ 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;
+    cerr << _np << npairs/(float)in_sz << endl;
+    cerr << "        avg # rank err: ";
+    cerr << rank_errors/(float)in_sz;
     if (faster_perceptron) cerr << " (meaningless)";
     cerr << endl;
-    cerr << "        avg # rank err: ";
-    cerr << rank_errors/(float)in_sz << endl;
     cerr << "     avg # margin viol: ";
     cerr << margin_violations/(float)in_sz << endl;
+    if (batch) cerr << "            batch loss: " << batch_loss << endl;
+    cerr << "       k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl;
     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;
@@ -510,9 +629,9 @@ main(int argc, char** argv)
 
   // write weights to file
   if (select_weights == "best" || keep) {
-    lambdas.init_vector(&dense_weights);
+    lambdas.init_vector(&decoder_weights);
     string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
-    Weights::WriteToFile(w_fn, dense_weights, true);
+    Weights::WriteToFile(w_fn, decoder_weights, true);
   }
 
   } // outer loop