From a5b856e83f23016540dea0bfe62a8a14132cee4b Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Tue, 29 Nov 2011 21:37:33 +0100
Subject: epoch averaging

---
 dtrain/README.md | 62 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 dtrain/dtrain.cc | 25 ++++++++++++++++++-----
 2 files changed, 82 insertions(+), 5 deletions(-)

(limited to 'dtrain')

diff --git a/dtrain/README.md b/dtrain/README.md
index d78dc100..32dafade 100644
--- a/dtrain/README.md
+++ b/dtrain/README.md
@@ -336,3 +336,65 @@ ioh:       4
 
 
 when does overfitting begin?
+---
+Variables
+ k 100..1500 higher better
+ N 3/4
+ learning rate
+ reg/gamma
+ epochs -> best on devtest (10..30) (select_weights)
+ scorer -> approx_bleu correlates ok (stupid bleu, bleu, smooth bleu)
+ sample from -> kbest | forest
+ filter -> no uniq (kbest)
+ pair sampling -> all 5050 108010 PRO alld
+ update_ok -> update towards correctly ranked
+ features
+  6x tm
+  2x lm
+  wp
+  Glue
+  rule ids
+  rule ngrams
+  rule shape
+  span features
+
+
+PRO
+ k = 1500
+ N = 4
+ learning rate = 0.0005
+ gamma = 0
+ epochs = 30
+ scorer = stupid bleu (Bleu+1)
+ sample from = kbest
+ filter = no
+ pair sampling = PRO
+ update_ok
+ features = base
+
+cur:
+ shard_sz 500 1k 3k
+ PRO with forest sampling
+ PRO w/o update_ok
+ tune learning rate
+ all with discard (not only top 50)
+ filter kbest uniq?
+
+ -> repeat most on Tset, lXlX stuff
+ -> PRO approx bleu
+ -> tune gamma
+ -> best pair sampling method
+ -> reduce k?
+ => scorer => approx_bleu (test w PRO)
+ -> PRO on training set
+ -> PRO more features 
+ -> discard + 108010
+
+
+
+--
+forest vs kbest count vocab?
+108010 select discard
+approx bleu
+
+
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 581c985a..e07b9307 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -30,6 +30,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("l1_reg",          po::value<string>()->default_value("none"),   "apply l1 regularization as in 'Tsuroka et al' (2010)")
     ("l1_reg_strength", po::value<weight_t>(),                                                  "l1 regularization strength")
     ("funny",           po::value<bool>()->zero_tokens(),                      "include correctly ranked pairs into updates")
+    ("average",         po::value<bool>()->zero_tokens(),                "output weight vector is average of all iterations")
 #ifdef DTRAIN_LOCAL
     ("refs,r",         po::value<string>(),                                                       "references in local mode")
 #endif
@@ -115,6 +116,9 @@ main(int argc, char** argv)
   bool funny = false;
   if (cfg.count("funny"))
     funny = true;
+  bool average = false;
+  if (cfg.count("average"))
+    average = true;
 
   const unsigned k = cfg["k"].as<unsigned>();
   const unsigned N = cfg["N"].as<unsigned>(); 
@@ -167,7 +171,7 @@ main(int argc, char** argv)
 
   // init weights
   vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
-  SparseVector<weight_t> lambdas, cumulative_penalties;
+  SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
   if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights);
   Weights::InitSparseVector(dense_weights, &lambdas);
 
@@ -480,6 +484,8 @@ if (false) {
 
   } // input loop
 
+  if (average) w_average += lambdas; 
+
   if (scorer_str == "approx_bleu") scorer->Reset();
 
   if (t == 0) {
@@ -572,20 +578,29 @@ if (false) {
 
   } // outer loop
 
+  if (average) w_average /= (weight_t)T;
+
 #ifndef DTRAIN_LOCAL
   unlink(grammar_buf_fn.c_str());
 #endif
 
   if (!noup) {
     if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl;
-    if (select_weights == "last") { // last
+    if (select_weights == "last" || average) { // last, average
       WriteFile of(output_fn); // works with '-'
       ostream& o = *of.stream();
       o.precision(17);
       o << _np;
-      for (SparseVector<weight_t>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
-	    if (it->second == 0) continue;
-        o << FD::Convert(it->first) << '\t' << it->second << endl;
+      if (average) {
+        for (SparseVector<weight_t>::const_iterator it = w_average.begin(); it != w_average.end(); ++it) {
+	      if (it->second == 0) continue;
+          o << FD::Convert(it->first) << '\t' << it->second << endl;
+        }
+      } else {
+        for (SparseVector<weight_t>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
+	      if (it->second == 0) continue;
+          o << FD::Convert(it->first) << '\t' << it->second << endl;
+        }
       }
     } else if (select_weights == "VOID") { // do nothing with the weights
     } else { // best
-- 
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