dtrain: rm 'check' and ksampler

3 files changed, 11 insertions, 41 deletions

diff --git a/training/dtrain/Makefile.am b/training/dtrain/Makefile.am
index 844c790d..3c072ffc 100644
--- a/training/dtrain/Makefile.am
+++ b/training/dtrain/Makefile.am
@@ -1,6 +1,6 @@
 bin_PROGRAMS = dtrain
 
-dtrain_SOURCES = dtrain.cc score.cc dtrain.h kbestget.h ksampler.h pairsampling.h score.h
+dtrain_SOURCES = dtrain.cc score.cc dtrain.h sample.h pairs.h score.h
 dtrain_LDADD   = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a
 
 AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 64fbf80d..67e16d23 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -1,8 +1,7 @@
 #include "dtrain.h"
 #include "score.h"
-#include "kbestget.h"
-#include "ksampler.h"
-#include "pairsampling.h"
+#include "sample.h"
+#include "pairs.h"
 
 using namespace dtrain;
 
@@ -13,15 +12,14 @@ dtrain_init(int argc, char** argv, po::variables_map* conf)
   po::options_description ini("Configuration File Options");
   ini.add_options()
     ("bitext,b",          po::value<string>(),                                            "bitext: 'src ||| tgt ||| 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")
+    ("output,o",          po::value<string>()->default_value("-"),                          "output weights file, '-' for STDOUT")
+    ("input_weights,w",   po::value<string>(),                                "input weights file (e.g. from previous iteration)")
+    ("decoder_config,d",  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")
     ("keep",              po::value<bool>()->zero_tokens(),                               "keep weights files for each iteration")
     ("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")
@@ -41,9 +39,8 @@ dtrain_init(int argc, char** argv, po::variables_map* conf)
     ("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")
-    ("check",             po::value<bool>()->zero_tokens(),                                  "produce list of loss differentials")
-    ("output_ranking",    po::value<string>()->default_value(""),                      "Output kbests with model scores and metric per iteration to this folder.")
-    ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
+    ("output_ranking",    po::value<string>()->default_value(""),                                   "output scored kbests to dir")
+    ("noup",              po::value<bool>()->zero_tokens(),                                                     "dont't optimize");
   po::options_description cl("Command Line Options");
   cl.add_options()
     ("config,c",         po::value<string>(),              "dtrain config file")
@@ -60,16 +57,6 @@ dtrain_init(int argc, char** argv, po::variables_map* conf)
     cerr << cl << endl;
     return false;
   }
-  if ((*conf)["sample_from"].as<string>() != "kbest"
-       && (*conf)["sample_from"].as<string>() != "forest") {
-    cerr << "Wrong 'sample_from' param: '" << (*conf)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
-    return false;
-  }
-  if ((*conf)["sample_from"].as<string>() == "kbest" && (*conf)["filter"].as<string>() != "uniq" &&
-        (*conf)["filter"].as<string>() != "not") {
-    cerr << "Wrong 'filter' param: '" << (*conf)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl;
-    return false;
-  }
   if ((*conf)["pair_sampling"].as<string>() != "all" && (*conf)["pair_sampling"].as<string>() != "XYX" &&
         (*conf)["pair_sampling"].as<string>() != "PRO" && (*conf)["pair_sampling"].as<string>() != "output_pairs") {
     cerr << "Wrong 'pair_sampling' param: '" << (*conf)["pair_sampling"].as<string>() << "'." << endl;
@@ -121,7 +108,6 @@ main(int argc, char** argv)
   const unsigned T = conf["epochs"].as<unsigned>();
   const unsigned stop_after = conf["stop_after"].as<unsigned>();
   const string filter_type = conf["filter"].as<string>();
-  const string sample_from = conf["sample_from"].as<string>();
   const string pair_sampling = conf["pair_sampling"].as<string>();
   const score_t pair_threshold = conf["pair_threshold"].as<score_t>();
   const string select_weights = conf["select_weights"].as<string>();
@@ -130,8 +116,6 @@ main(int argc, char** argv)
   const score_t approx_bleu_d = conf["approx_bleu_d"].as<score_t>();
   const unsigned max_pairs = conf["max_pairs"].as<unsigned>();
   int repeat = conf["repeat"].as<unsigned>();
-  bool check = false;
-  if (conf.count("check")) check = true;
   weight_t loss_margin = conf["loss_margin"].as<weight_t>();
   bool batch = false;
   if (conf.count("batch")) batch = true;
@@ -183,10 +167,7 @@ main(int argc, char** argv)
   // setup decoder observer
   MT19937 rng; // random number generator, only for forest sampling
   HypSampler* observer;
-  if (sample_from == "kbest")
-    observer = static_cast<KBestGetter*>(new KBestGetter(k, filter_type));
-  else
-    observer = static_cast<KSampler*>(new KSampler(k, &rng));
+  observer = static_cast<KBestGetter*>(new KBestGetter(k, filter_type));
   observer->SetScorer(scorer);
 
   // init weights
@@ -244,9 +225,7 @@ main(int argc, char** argv)
     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;
-    if (sample_from == "kbest")
-      cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
+    cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
     cerr << setw(25) << "learning rate " << eta << endl;
     cerr << setw(25) << "gamma " << gamma << endl;
     cerr << setw(25) << "loss margin " << loss_margin << endl;
@@ -408,9 +387,6 @@ main(int argc, char** argv)
 
       score_t kbest_loss_first = 0.0, kbest_loss_last = 0.0;
 
-      if (check) repeat = 2;
-      vector<float> losses; // for check
-
       if (pair_sampling == "output_pairs") {
         for (auto p: pairs) {
           cout << p.first.model << " ||| "  << p.first.score << " ||| " <<  p.first.f  << endl;
@@ -428,7 +404,6 @@ main(int argc, char** argv)
         }
         score_t model_diff = it->first.model - it->second.model;
         score_t loss = max(0.0, -1.0 * model_diff);
-        losses.push_back(loss);
         kbest_loss_first += loss;
       }
 
@@ -439,15 +414,11 @@ main(int argc, char** argv)
       SparseVector<weight_t> sum_up; // for pclr
       if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas;
 
-      unsigned pair_idx = 0; // for check
       for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
            it != pairs.end(); it++) {
         score_t model_diff = it->first.model - it->second.model;
         score_t loss = max(0.0, -1.0 * model_diff);
 
-        if (check && ki==repeat-1) cout << losses[pair_idx] - loss << endl;
-        pair_idx++;
-
         if (repeat > 1) {
           model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f);
           kbest_loss += loss;
@@ -649,8 +620,6 @@ main(int argc, char** argv)
     Weights::WriteToFile(w_fn, decoder_weights, true);
   }
 
-  if (check) cout << "---" << endl;
-
   } // outer loop
 
   if (average) w_average /= (weight_t)T;
diff --git a/training/dtrain/dtrain.h b/training/dtrain/dtrain.h
index d7980688..e25c6f24 100644
--- a/training/dtrain/dtrain.h
+++ b/training/dtrain/dtrain.h
@@ -15,6 +15,7 @@
 
 #include "decoder.h"
 #include "ff_register.h"
+#include "sampler.h"
 #include "sentence_metadata.h"
 #include "verbose.h"
 #include "viterbi.h"