dtrain: output dataHEADmaster

3 files changed, 105 insertions, 27 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 9ca048c0..b39fff3e 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -12,17 +12,20 @@ main(int argc, char** argv)
   po::variables_map conf;
   if (!dtrain_init(argc, argv, &conf))
     return 1;
-  const size_t k              = conf["k"].as<size_t>();
-  const string score_name     = conf["score"].as<string>();
-  const size_t N              = conf["N"].as<size_t>();
-  const size_t T              = conf["iterations"].as<size_t>();
-  const weight_t eta          = conf["learning_rate"].as<weight_t>();
-  const weight_t margin       = conf["margin"].as<weight_t>();
-  const bool average          = conf["average"].as<bool>();
-  const bool structured       = conf["struct"].as<bool>();
-  const weight_t l1_reg       = conf["l1_reg"].as<weight_t>();
-  const bool keep             = conf["keep"].as<bool>();
-  const string output_fn      = conf["output"].as<string>();
+  const size_t k                 = conf["k"].as<size_t>();
+  const string score_name        = conf["score"].as<string>();
+  const size_t N                 = conf["N"].as<size_t>();
+  const size_t T                 = conf["iterations"].as<size_t>();
+  const weight_t eta             = conf["learning_rate"].as<weight_t>();
+  const weight_t margin          = conf["margin"].as<weight_t>();
+  const bool average             = conf["average"].as<bool>();
+  const bool structured          = conf["struct"].as<bool>();
+  const weight_t l1_reg          = conf["l1_reg"].as<weight_t>();
+  const bool keep                = conf["keep"].as<bool>();
+  const bool noup                = conf["disable_learning"].as<bool>();
+  const string output_fn         = conf["output"].as<string>();
+  const string output_data_which = conf["output_data"].as<string>();
+  const bool output_data         = output_data_which!="";
   vector<string> print_weights;
   boost::split(print_weights, conf["print_weights"].as<string>(),
                boost::is_any_of(" "));
@@ -162,7 +165,19 @@ main(int argc, char** argv)
     feature_count += observer->GetFeatureCount();
     list_sz += observer->GetSize();
 
+    if (output_data) {
+      if (output_data_which == "kbest") {
+        OutputKbest(samples);
+      } else if (output_data_which == "default") {
+        OutputMultipartitePairs(samples, margin);
+      } else if (output_data_which == "all") {
+        OutputAllPairs(samples);
+      }
+    }
+
     // get pairs and update
+    if (!noup) {
+
     SparseVector<weight_t> updates;
     if (structured)
       num_up += CollectUpdatesStruct(samples, updates);
@@ -204,6 +219,8 @@ main(int argc, char** argv)
       }
     }
 
+    } // noup
+
     i++;
 
   } // input loop
diff --git a/training/dtrain/dtrain.h b/training/dtrain/dtrain.h
index 6633b4f9..0bbb5c9b 100644
--- a/training/dtrain/dtrain.h
+++ b/training/dtrain/dtrain.h
@@ -48,22 +48,24 @@ dtrain_init(int argc, char** argv, po::variables_map* conf)
 {
   po::options_description opts("Configuration File Options");
   opts.add_options()
-    ("bitext,b",          po::value<string>(),                                                  "bitext")
-    ("decoder_conf,C",    po::value<string>(),                          "configuration file for decoder")
-    ("iterations,T",      po::value<size_t>()->default_value(15),   "number of iterations T (per shard)")
-    ("k",                 po::value<size_t>()->default_value(100),                  "size of kbest list")
-    ("learning_rate,l",   po::value<weight_t>()->default_value(0.00001),                 "learning rate")
-    ("l1_reg,r",          po::value<weight_t>()->default_value(0.),         "l1 regularization strength")
-    ("margin,m",          po::value<weight_t>()->default_value(1.0),      "margin for margin perceptron")
-    ("score,s",           po::value<string>()->default_value("chiang"),      "per-sentence BLEU approx.")
-    ("N",                 po::value<size_t>()->default_value(4),              "N for BLEU approximation")
-    ("input_weights,w",   po::value<string>(),                                      "input weights file")
-    ("average,a",         po::bool_switch()->default_value(true),               "output average weights")
-    ("keep,K",            po::bool_switch()->default_value(false),  "output a weight file per iteration")
-    ("struct,S",          po::bool_switch()->default_value(false),       "structured SGD with hope/fear")
-    ("output,o",          po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
-    ("print_weights,P",   po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV"),
-                                                         "list of weights to print after each iteration");
+    ("bitext,b",           po::value<string>(),                                                      "bitext")
+    ("decoder_conf,C",     po::value<string>(),                              "configuration file for decoder")
+    ("iterations,T",       po::value<size_t>()->default_value(15),       "number of iterations T (per shard)")
+    ("k",                  po::value<size_t>()->default_value(100),                      "size of kbest list")
+    ("learning_rate,l",    po::value<weight_t>()->default_value(0.00001),                     "learning rate")
+    ("l1_reg,r",           po::value<weight_t>()->default_value(0.),             "l1 regularization strength")
+    ("margin,m",           po::value<weight_t>()->default_value(1.0),          "margin for margin perceptron")
+    ("score,s",            po::value<string>()->default_value("chiang"),          "per-sentence BLEU approx.")
+    ("N",                  po::value<size_t>()->default_value(4),                  "N for BLEU approximation")
+    ("input_weights,w",    po::value<string>(),                                          "input weights file")
+    ("average,a",          po::bool_switch()->default_value(true),                   "output average weights")
+    ("keep,K",             po::bool_switch()->default_value(false),      "output a weight file per iteration")
+    ("struct,S",           po::bool_switch()->default_value(false),           "structured SGD with hope/fear")
+    ("output,o",           po::value<string>()->default_value("-"),     "output weights file, '-' for STDOUT")
+    ("disable_learning,X", po::bool_switch()->default_value(false),                        "disable learning")
+    ("output_data,D",      po::value<string>()->default_value(""), "output data to STDOUT; arg. is 'kbest', 'default' or 'all'")
+    ("print_weights,P",    po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV"),
+                                                             "list of weights to print after each iteration");
   po::options_description clopts("Command Line Options");
   clopts.add_options()
     ("conf,c", po::value<string>(), "dtrain configuration file")
@@ -93,6 +95,15 @@ dtrain_init(int argc, char** argv, po::variables_map* conf)
 
     return false;
   }
+  if ((*conf)["output_data"].as<string>() != "") {
+    if ((*conf)["output_data"].as<string>() != "kbest" &&
+        (*conf)["output_data"].as<string>() != "default" &&
+        (*conf)["output_data"].as<string>() != "all") {
+      cerr << "Wrong 'output_data' argument: ";
+      cerr << (*conf)["output_data"].as<string>() << endl;
+      return false;
+    }
+  }
 
   return true;
 }
diff --git a/training/dtrain/update.h b/training/dtrain/update.h
index 6f42e5bd..83dc3186 100644
--- a/training/dtrain/update.h
+++ b/training/dtrain/update.h
@@ -94,6 +94,56 @@ CollectUpdatesStruct(vector<ScoredHyp>* s,
   return updates.size();
 }
 
+inline void
+OutputKbest(vector<ScoredHyp>* s)
+{
+  sort(s->begin(), s->end(), _cmp);
+  size_t i = 0;
+  for (auto k: *s) {
+    cout << i << "\t" << k.gold << "\t" << k.model << " \t" << k.f << endl;
+    i++;
+  }
+}
+
+inline void
+OutputMultipartitePairs(vector<ScoredHyp>* s,
+                               weight_t margin=0.,
+                               bool all=true)
+{
+  size_t sz = s->size();
+  sort(s->begin(), s->end(), _cmp);
+  size_t sep = round(sz*0.1);
+  for (size_t i = 0; i < sep; i++) {
+    for (size_t j = sep; j < sz; j++) {
+      if (!all && _good((*s)[i], (*s)[j], margin))
+        continue;
+      cout << (*s)[i].f-(*s)[j].f << endl;
+    }
+  }
+  size_t sep_lo = sz-sep;
+  for (size_t i = sep; i < sep_lo; i++) {
+    for (size_t j = sep_lo; j < sz; j++) {
+      if (!all && _good((*s)[i], (*s)[j], margin))
+        continue;
+      cout << (*s)[i].f-(*s)[j].f << endl;
+    }
+  }
+}
+
+inline void
+OutputAllPairs(vector<ScoredHyp>* s)
+{
+  size_t sz = s->size();
+  sort(s->begin(), s->end(), _cmp);
+  for (size_t i = 0; i < sz-1; i++) {
+    for (size_t j = i+1; j < sz; j++) {
+      if ((*s)[i].gold == (*s)[j].gold)
+        continue;
+      cout << (*s)[i].f-(*s)[j].f << endl;
+    }
+  }
+}
+
 } // namespace
 
 #endif