From 9ba06c6f1a7e751da245219da291e329efa2b7e5 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Wed, 14 Sep 2011 13:12:01 +0100
Subject: fix pro train bug causing it not to optimize when there is no
 held-out test set

---
 pro-train/mr_pro_reduce.cc | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

(limited to 'pro-train')

diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
index 239649c1..e71347ba 100644
--- a/pro-train/mr_pro_reduce.cc
+++ b/pro-train/mr_pro_reduce.cc
@@ -194,7 +194,6 @@ int main(int argc, char** argv) {
   InitCommandLine(argc, argv, &conf);
   string line;
   vector<pair<bool, SparseVector<weight_t> > > training, testing;
-  SparseVector<weight_t> old_weights;
   const bool tune_regularizer = conf.count("tune_regularizer");
   if (tune_regularizer && !conf.count("testset")) {
     cerr << "--tune_regularizer requires --testset to be set\n";
@@ -202,28 +201,28 @@ int main(int argc, char** argv) {
   }
   const double min_reg = conf["min_reg"].as<double>();
   const double max_reg = conf["max_reg"].as<double>();
-  double sigsq = conf["sigma_squared"].as<double>();
+  double sigsq = conf["sigma_squared"].as<double>(); // will be overridden if parameter is tuned
   assert(sigsq > 0.0);
   assert(min_reg > 0.0);
   assert(max_reg > 0.0);
   assert(max_reg > min_reg);
   const double psi = conf["interpolation"].as<double>();
   if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; }
-  if (conf.count("weights")) {
-    vector<weight_t> dt;
-    Weights::InitFromFile(conf["weights"].as<string>(), &dt);
-    Weights::InitSparseVector(dt, &old_weights);
-  }
   ReadCorpus(&cin, &training);
   if (conf.count("testset")) {
     ReadFile rf(conf["testset"].as<string>());
     ReadCorpus(rf.stream(), &testing);
   }
   cerr << "Number of features: " << FD::NumFeats() << endl;
-  vector<weight_t> x(FD::NumFeats(), 0.0);  // x[0] is bias
-  for (SparseVector<weight_t>::const_iterator it = old_weights.begin();
-       it != old_weights.end(); ++it)
-    x[it->first] = it->second;
+
+  vector<weight_t> x, prev_x;  // x[0] is bias
+  if (conf.count("weights")) {
+    Weights::InitFromFile(conf["weights"].as<string>(), &x);
+    prev_x = x;
+  }
+  cerr << "         Number of features: " << x.size() << endl;
+  cerr << "Number of training examples: " << training.size() << endl;
+  cerr << "Number of  testing examples: " << testing.size() << endl;
   double tppl = 0.0;
   vector<pair<double,double> > sp;
   vector<double> smoothed;
@@ -255,11 +254,12 @@ int main(int argc, char** argv) {
       }
     }
     sigsq = sp[best_i].first;
-    tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
-  }
+  }  // tune regularizer
+  tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
   if (conf.count("weights")) {
-    for (int i = 1; i < x.size(); ++i)
-      x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi);
+    for (int i = 1; i < x.size(); ++i) {
+      x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi);
+    }
   }
   cout.precision(15);
   cout << "# sigma^2=" << sigsq << "\theld out perplexity=";
-- 
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