From 4d90fcf6a24be10aa35c7783e6f2623e9fedf9d6 Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Tue, 10 Sep 2013 18:20:16 +0200
Subject: simple pclr

---
 training/dtrain/dtrain.cc | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

(limited to 'training/dtrain')

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 0ee2f124..34c0a54a 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -40,6 +40,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("scale_bleu_diff",   po::value<bool>()->zero_tokens(),                      "learning rate <- bleu diff of a misranked pair")
     ("loss_margin",       po::value<weight_t>()->default_value(0.),  "update if no error in pref pair but model scores this near")
     ("max_pairs",         po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.")
+    ("pclr",              po::value<bool>()->zero_tokens(),                         "use a (simple) per-coordinate learning rate")
     ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
   po::options_description cl("Command Line Options");
   cl.add_options()
@@ -124,6 +125,8 @@ main(int argc, char** argv)
   if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max();
   bool scale_bleu_diff = false;
   if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
+  bool pclr = false;
+  if (cfg.count("pclr")) pclr = true;
   bool average = false;
   if (select_weights == "avg")
     average = true;
@@ -131,7 +134,6 @@ main(int argc, char** argv)
   if (cfg.count("print_weights"))
     boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
 
-
   // setup decoder
   register_feature_functions();
   SetSilent(true);
@@ -249,6 +251,8 @@ main(int argc, char** argv)
       cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
     if (rescale)
       cerr << setw(25) << "rescale " << rescale << endl;
+    if (pclr)
+      cerr << setw(25) << "pclr " << pclr << endl;
     cerr << setw(25) << "max pairs " << max_pairs << endl;
     cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
     cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
@@ -261,6 +265,8 @@ main(int argc, char** argv)
     if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
   }
 
+  // pclr
+  SparseVector<weight_t> learning_rates;
 
   for (unsigned t = 0; t < T; t++) // T epochs
   {
@@ -385,7 +391,16 @@ main(int argc, char** argv)
         if (scale_bleu_diff) eta = it->first.score - it->second.score;
         if (rank_error || margin < loss_margin) {
           SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
-          lambdas.plus_eq_v_times_s(diff_vec, eta);
+          if (pclr) {
+            SparseVector<weight_t>::iterator jt = diff_vec.begin();
+            for (; jt != diff_vec.end(); ++it) {
+              jt->second *= max(0.0000001, eta/(eta+learning_rates[jt->first])); // FIXME
+              learning_rates[jt->first]++;
+            }
+            lambdas += diff_vec;
+            } else {
+              lambdas.plus_eq_v_times_s(diff_vec, eta);
+            }
           if (gamma)
             lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
         }
@@ -395,14 +410,14 @@ main(int argc, char** argv)
       // please note that this regularizations happen
       // after a _sentence_ -- not after each example/pair!
       if (l1naive) {
-        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        SparseVector<weight_t>::iterator it = lambdas.begin();
         for (; it != lambdas.end(); ++it) {
           if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
             it->second -= sign(it->second) * l1_reg;
           }
         }
       } else if (l1clip) {
-        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        SparseVector<weight_t>::iterator it = lambdas.begin();
         for (; it != lambdas.end(); ++it) {
           if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
             if (it->second != 0) {
@@ -417,7 +432,7 @@ main(int argc, char** argv)
         }
       } else if (l1cumul) {
         weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
-        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        SparseVector<weight_t>::iterator it = lambdas.begin();
         for (; it != lambdas.end(); ++it) {
           if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
             if (it->second != 0) {
-- 
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