randomize order of samples in mira

1 files changed, 27 insertions, 5 deletions

diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc
index ae54c807..6918a9a1 100644
--- a/mira/kbest_mira.cc
+++ b/mira/kbest_mira.cc
@@ -23,12 +23,14 @@
 #include "fdict.h"
 #include "weights.h"
 #include "sparse_vector.h"
+#include "sampler.h"
 
 using namespace std;
 using boost::shared_ptr;
 namespace po = boost::program_options;
 
 bool invert_score;
+boost::shared_ptr<MT19937> rng;
 
 void SanityCheck(const vector<double>& w) {
   for (int i = 0; i < w.size(); ++i) {
@@ -45,6 +47,17 @@ struct FComp {
   }
 };
 
+void RandomPermutation(int len, vector<int>* p_ids) {
+  vector<int>& ids = *p_ids;
+  ids.resize(len);
+  for (int i = 0; i < len; ++i) ids[i] = i;
+  for (int i = len; i > 0; --i) {
+    int j = rng->next() * i;
+    if (j == i) i--;
+    swap(ids[i-1], ids[j]);
+  }  
+}
+
 void ShowLargestFeatures(const vector<double>& w) {
   vector<int> fnums(w.size());
   for (int i = 0; i < w.size(); ++i)
@@ -71,6 +84,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
         ("max_step_size,C", po::value<double>()->default_value(0.01), "regularization strength (C)")
         ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by")
         ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
         ("decoder_config,c",po::value<string>(),"Decoder configuration file");
   po::options_description clo("Command line options");
   clo.add_options()
@@ -176,6 +190,10 @@ int main(int argc, char** argv) {
   po::variables_map conf;
   if (!InitCommandLine(argc, argv, &conf)) return 1;
 
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
   vector<string> corpus;
   ReadTrainingCorpus(conf["source"].as<string>(), &corpus);
   const string metric_name = conf["mt_metric"].as<string>();
@@ -219,6 +237,8 @@ int main(int argc, char** argv) {
   int max_iteration = conf["passes"].as<int>() * corpus.size();
   string msg = "# MIRA tuned weights";
   string msga = "# MIRA tuned weights AVERAGED";
+  vector<int> order;
+  RandomPermutation(corpus.size(), &order);
   while (lcount <= max_iteration) {
     dense_weights.clear();
     weights.InitFromVector(lambdas);
@@ -242,14 +262,16 @@ int main(int argc, char** argv) {
       ww.InitFromVector(x);
       ww.WriteToFile(sa.str(), true, &msga);
       ++cur_pass;
-    } else if (cur_sent == 0) {
+      RandomPermutation(corpus.size(), &order);
+    }
+    if (cur_sent == 0) {
       cerr << "PASS " << (lcount / corpus.size() + 1) << endl;
     }
-    decoder.SetId(cur_sent);
-    decoder.Decode(corpus[cur_sent], &observer);  // update oracles
+    decoder.SetId(order[cur_sent]);
+    decoder.Decode(corpus[order[cur_sent]], &observer);  // update oracles
     const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis();
-    const HypothesisInfo& cur_good = *oracles[cur_sent].good;
-    const HypothesisInfo& cur_bad = *oracles[cur_sent].bad;
+    const HypothesisInfo& cur_good = *oracles[order[cur_sent]].good;
+    const HypothesisInfo& cur_bad = *oracles[order[cur_sent]].bad;
     tot_loss += cur_hyp.mt_metric;
     if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) {
       const double loss = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights) +