From 3b371a950f9ae04c9072f3df9b21dafa475916fa Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Thu, 29 Jan 2015 19:50:27 +0100
Subject: dtrain: output_pairs, print_ranking

---
 training/dtrain/dtrain.cc | 27 +++++++++++++++++++++++++--
 training/dtrain/score.cc  |  2 +-
 2 files changed, 26 insertions(+), 3 deletions(-)

(limited to 'training')

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 737326f8..0b648d95 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -41,8 +41,9 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("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")
+    ("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")
+    ("print_ranking",     po::value<bool>()->zero_tokens(),                            "output kbest with model score and metric")
     ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
   po::options_description cl("Command Line Options");
   cl.add_options()
@@ -71,7 +72,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     return false;
   }
   if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" &&
-        (*cfg)["pair_sampling"].as<string>() != "PRO") {
+        (*cfg)["pair_sampling"].as<string>() != "PRO" && (*cfg)["pair_sampling"].as<string>() != "output_pairs") {
     cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
     return false;
   }
@@ -114,6 +115,8 @@ main(int argc, char** argv)
   if (cfg.count("rescale")) rescale = true;
   bool keep = false;
   if (cfg.count("keep")) keep = true;
+  bool print_ranking = false;
+  if (cfg.count("print_ranking")) print_ranking = true;
 
   const unsigned k = cfg["k"].as<unsigned>();
   const unsigned N = cfg["N"].as<unsigned>();
@@ -356,6 +359,14 @@ main(int argc, char** argv)
     // get (scored) samples
     vector<ScoredHyp>* samples = observer->GetSamples();
 
+    if (print_ranking) {
+      for (auto s: *samples) {
+        cout << ii << " ||| ";
+        printWordIDVec(s.w, cout);
+        cout << " ||| " << s.model << " ||| " << s.score << endl;
+      }
+    }
+
     if (verbose) {
       cerr << "--- refs for " << ii << ": ";
       for (auto r: refs_as_ids_buf[ii]) {
@@ -389,6 +400,8 @@ 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);
+      if (pair_sampling == "output_pairs")
+        all_pairs(samples, pairs, pair_threshold, max_pairs, false);
       int cur_npairs = pairs.size();
       npairs += cur_npairs;
 
@@ -397,6 +410,15 @@ main(int argc, char** argv)
       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;
+          cout << p.second.model << " ||| "  << p.second.score << " ||| " <<  p.second.f  << endl;
+          cout << endl;
+        }
+        continue;
+      }
+
       for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
            it != pairs.end(); it++) {
         score_t model_diff = it->first.model - it->second.model;
@@ -547,6 +569,7 @@ main(int argc, char** argv)
 
   if (t == 0) in_sz = ii; // remember size of input (# lines)
 
+  if (print_ranking) cout << "---" << endl;
 
   if (batch) {
     lambdas.plus_eq_v_times_s(batch_updates, eta);
diff --git a/training/dtrain/score.cc b/training/dtrain/score.cc
index d81eafcb..8a28771f 100644
--- a/training/dtrain/score.cc
+++ b/training/dtrain/score.cc
@@ -36,7 +36,7 @@ RefLen(vector<vector<WordID> > refs)
 {
   size_t ref_len = 0;
   for (auto r: refs)
-    ref_len = max(ref_len, r.size());
+    ref_len = max(ref_len, r.size()); // FIXME
   return ref_len;
 }
 
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