From 37b9e45e5cb29d708f7249dbe0b0fb27685282a0 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Thu, 11 Oct 2012 14:06:12 -0400
Subject: produce alignments for a test set (which can be stdin)

---
 training/fast_align.cc | 40 +++++++++++++++++++++++++---------------
 1 file changed, 25 insertions(+), 15 deletions(-)

(limited to 'training')

diff --git a/training/fast_align.cc b/training/fast_align.cc
index 0d7b0202..7492d26f 100644
--- a/training/fast_align.cc
+++ b/training/fast_align.cc
@@ -29,6 +29,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
         ("no_null_word,N","Do not generate from a null token")
         ("output_parameters,p", "Write model parameters instead of alignments")
         ("beam_threshold,t",po::value<double>()->default_value(-4),"When writing parameters, log_10 of beam threshold for writing parameter (-10000 to include everything, 0 max parameter only)")
+        ("hide_training_alignments,H", "Hide training alignments (only useful if you want to use -x option and just compute testset statistics)")
         ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model")
         ("no_add_viterbi,V","When writing model parameters, do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)");
   po::options_description clo("Command line options");
@@ -54,14 +55,6 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   return true;
 }
 
-double PosteriorInference(const vector<WordID>& src, const vector<WordID>& trg) {
-  double llh = 0;
-  static vector<double> unnormed_a_i;
-  if (src.size() > unnormed_a_i.size())
-    unnormed_a_i.resize(src.size());
-  return llh;
-}
-
 int main(int argc, char** argv) {
   po::variables_map conf;
   if (!InitCommandLine(argc, argv, &conf)) return 1;
@@ -76,6 +69,7 @@ int main(int argc, char** argv) {
   const bool write_alignments = (conf.count("output_parameters") == 0);
   const double diagonal_tension = conf["diagonal_tension"].as<double>();
   const double prob_align_null = conf["prob_align_null"].as<double>();
+  const bool hide_training_alignments = (conf.count("hide_training_alignments") > 0);
   string testset;
   if (conf.count("testset")) testset = conf["testset"].as<string>();
   const double prob_align_not_null = 1.0 - prob_align_null;
@@ -164,7 +158,7 @@ int main(int argc, char** argv) {
                 max_i = src[i-1];
               }
             }
-            if (write_alignments) {
+            if (!hide_training_alignments && write_alignments) {
               if (max_index > 0) {
                 if (first_al) first_al = false; else cout << ' ';
                 if (reverse)
@@ -183,7 +177,7 @@ int main(int argc, char** argv) {
         }
         likelihood += log(sum);
       }
-      if (write_alignments && final_iteration) cout << endl;
+      if (write_alignments && final_iteration && !hide_training_alignments) cout << endl;
     }
 
     // log(e) = 1.0
@@ -210,11 +204,13 @@ int main(int argc, char** argv) {
     istream& in = *rf.stream();
     int lc = 0;
     double tlp = 0;
-    string ssrc, strg, line;
+    string line;
     while (getline(in, line)) {
       ++lc;
       vector<WordID> src, trg;
       CorpusTools::ReadLine(line, &src, &trg);
+      cout << TD::GetString(src) << " ||| " << TD::GetString(trg) << " |||";
+      if (reverse) swap(src, trg);
       double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
       if (src.size() > unnormed_a_i.size())
         unnormed_a_i.resize(src.size());
@@ -223,11 +219,14 @@ int main(int argc, char** argv) {
       for (int j = 0; j < trg.size(); ++j) {
         const WordID& f_j = trg[j];
         double sum = 0;
+        int a_j = 0;
+        double max_pat = 0;
         const double j_over_ts = double(j) / trg.size();
         double prob_a_i = 1.0 / (src.size() + use_null);  // uniform (model 1)
         if (use_null) {
           if (favor_diagonal) prob_a_i = prob_align_null;
-          sum += s2t.prob(kNULL, f_j) * prob_a_i;
+          max_pat = s2t.prob(kNULL, f_j) * prob_a_i;
+          sum += max_pat;
         }
         double az = 0;
         if (favor_diagonal) {
@@ -240,13 +239,24 @@ int main(int argc, char** argv) {
         for (int i = 1; i <= src.size(); ++i) {
           if (favor_diagonal)
             prob_a_i = unnormed_a_i[i-1] / az;
-          sum += s2t.prob(src[i-1], f_j) * prob_a_i;
+          double pat = s2t.prob(src[i-1], f_j) * prob_a_i;
+          if (pat > max_pat) { max_pat = pat; a_j = i; }
+          sum += pat;
         }
         log_prob += log(sum);
+        if (write_alignments) {
+          if (a_j > 0) {
+            cout << ' ';
+            if (reverse)
+              cout << j << '-' << (a_j - 1);
+            else
+              cout << (a_j - 1) << '-' << j;
+          }
+        }
       }
       tlp += log_prob;
-      cerr << ssrc << " ||| " << strg << " ||| " << log_prob << endl;
-    }
+      cout << " ||| " << log_prob << endl << flush;
+    } // loop over test set sentences
     cerr << "TOTAL LOG PROB " << tlp << endl;
   }
 
-- 
cgit v1.2.3