added params, output

1 files changed, 20 insertions, 9 deletions

diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index e817e7ab..b662cd26 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -21,17 +21,18 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("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")
+    ("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_")
     ("learning_rate",     po::value<weight_t>()->default_value(0.0001),                                        "learning rate")
-    ("gamma",             po::value<weight_t>()->default_value(0),                          "gamma for SVM (0 for perceptron)")
+    ("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),   "TODO select top x percent of features after each epoch")
+    ("fselect",           po::value<weight_t>()->default_value(-1), "TODO select top x percent (or by threshold) of features after each epoch")
     ("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")
 #ifdef DTRAIN_LOCAL
     ("refs,r",            po::value<string>(),                                                      "references in local mode")
 #endif
@@ -133,6 +134,8 @@ main(int argc, char** argv)
   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>();
+  bool scale_bleu_diff = false;
+  if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
   bool average = false;
   if (select_weights == "avg")
     average = true;
@@ -236,7 +239,8 @@ main(int argc, char** argv)
     cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
     if (sample_from == "kbest")
       cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
-    cerr << setw(25) << "learning rate " << eta << 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) << "pairs " << "'" << pair_sampling << "'" << endl;
     if (pair_sampling == "XYX")
@@ -255,7 +259,7 @@ main(int argc, char** argv)
     cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
     if (cfg.count("input_weights"))
       cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
-    if (cfg.count("stop-after"))
+    if (stop_after > 0)
       cerr << setw(25) << "stop_after " << stop_after << endl;
     if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
   }
@@ -274,7 +278,7 @@ main(int argc, char** argv)
 #endif
   score_t score_sum = 0.;
   score_t model_sum(0);
-  unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 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)
@@ -392,7 +396,7 @@ main(int argc, char** argv)
       else printWordIDVec(ref_ids);
       cerr << endl;
       for (unsigned u = 0; u < samples->size(); u++) {
-        cerr << _p5 << _np << "[" << u << ". '";
+        cerr << _p2 << _np << "[" << u << ". '";
         printWordIDVec((*samples)[u].w);
         cerr << "'" << endl;
         cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl;
@@ -403,8 +407,12 @@ main(int argc, char** argv)
     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);
@@ -420,6 +428,7 @@ main(int argc, char** argv)
         if (rank_error) rank_errors++;
         score_t margin = fabs(it->first.model - it->second.model);
         if (!rank_error && margin < 1) margin_violations++;
+        if (scale_bleu_diff) eta = it->first.score - it->second.score;
         if (rank_error || (gamma && margin<1)) {
           SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
           lambdas.plus_eq_v_times_s(diff_vec, eta);
@@ -512,7 +521,7 @@ main(int argc, char** argv)
   if (!quiet || hstreaming) nonz = (unsigned)lambdas.size_nonzero();
 
   if (!quiet) {
-    cerr << _p9 << _p << "WEIGHTS" << endl;
+    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;
     }
@@ -528,6 +537,8 @@ main(int argc, char** argv)
     cerr << "     avg # margin viol: ";
     cerr << margin_violations/(float)in_sz << 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;
   }
 
   if (hstreaming) {
@@ -617,7 +628,7 @@ main(int argc, char** argv)
   if (!quiet) {
     cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
     cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
-    cerr << _p2 << "This took " << overall_time/60. << " min." << endl;
+    cerr << "This took " << overall_time/60. << " min." << endl;
   }
 }