From 5dab10306f27318b574ebc6312e23d46fdc67f71 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Thu, 5 May 2011 15:57:00 -0400
Subject: print avg weights at every iteration

---
 mira/kbest_mira.cc | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

(limited to 'mira/kbest_mira.cc')

diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc
index 60703273..ae54c807 100644
--- a/mira/kbest_mira.cc
+++ b/mira/kbest_mira.cc
@@ -53,6 +53,7 @@ void ShowLargestFeatures(const vector<double>& w) {
   mid += (w.size() > 10 ? 10 : w.size());
   partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
   cerr << "TOP FEATURES:";
+  --mid;
   for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
     cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
   }
@@ -207,15 +208,17 @@ int main(int argc, char** argv) {
   TrainingObserver observer(conf["k_best_size"].as<int>(), ds, &oracles);
   int cur_sent = 0;
   int lcount = 0;
+  int normalizer = 0;
   double tot_loss = 0;
   int dots = 0;
   int cur_pass = 0;
   vector<double> dense_weights;
   SparseVector<double> tot;
   tot += lambdas;          // initial weights
-  lcount++;                // count for initial weights
+  normalizer++;            // count for initial weights
   int max_iteration = conf["passes"].as<int>() * corpus.size();
   string msg = "# MIRA tuned weights";
+  string msga = "# MIRA tuned weights AVERAGED";
   while (lcount <= max_iteration) {
     dense_weights.clear();
     weights.InitFromVector(lambdas);
@@ -223,16 +226,25 @@ int main(int argc, char** argv) {
     decoder.SetWeights(dense_weights);
     if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
     if (corpus.size() == cur_sent) {
-      cur_sent = 0;
       cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
+      ShowLargestFeatures(dense_weights);
+      cur_sent = 0;
       tot_loss = 0;
       dots = 0;
       ostringstream os;
       os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz";
       weights.WriteToFile(os.str(), true, &msg);
+      SparseVector<double> x = tot;
+      x /= normalizer;
+      ostringstream sa;
+      sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz";
+      Weights ww;
+      ww.InitFromVector(x);
+      ww.WriteToFile(sa.str(), true, &msga);
       ++cur_pass;
+    } else if (cur_sent == 0) {
+      cerr << "PASS " << (lcount / corpus.size() + 1) << endl;
     }
-    if (cur_sent == 0) { cerr << "PASS " << (lcount / corpus.size() + 1) << endl << lambdas << endl; }
     decoder.SetId(cur_sent);
     decoder.Decode(corpus[cur_sent], &observer);  // update oracles
     const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis();
@@ -255,16 +267,17 @@ int main(int argc, char** argv) {
       }
     }
     tot += lambdas;
+    ++normalizer;
     ++lcount;
     ++cur_sent;
   }
   cerr << endl;
   weights.WriteToFile("weights.mira-final.gz", true, &msg);
-  tot /= lcount;
+  tot /= normalizer;
   weights.InitFromVector(tot);
   msg = "# MIRA tuned weights (averaged vector)";
   weights.WriteToFile("weights.mira-final-avg.gz", true, &msg);
-  cerr << "Optimization complete.\\AVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
+  cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
   return 0;
 }
 
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