dtrain: fix_features

1 files changed, 19 insertions, 2 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index b180bc82..ae5b630a 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -44,6 +44,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("repeat",            po::value<unsigned>()->default_value(1),     "repeat optimization over kbest list this number of times")
     ("check",             po::value<bool>()->zero_tokens(),                                  "produce list of loss differentials")
     ("output_ranking",    po::value<string>()->default_value(""),                      "Output kbests with model scores and metric per iteration to this folder.")
+    ("fix_features",      po::value<bool>()->zero_tokens(),                  "Ignore all features that are not in input_weights.")
     ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
   po::options_description cl("Command Line Options");
   cl.add_options()
@@ -115,6 +116,8 @@ main(int argc, char** argv)
   if (cfg.count("rescale")) rescale = true;
   bool keep = false;
   if (cfg.count("keep")) keep = true;
+  bool fix_features = false;
+  if (cfg.count("fix_features")) fix_features = true;
 
   const unsigned k = cfg["k"].as<unsigned>();
   const unsigned N = cfg["N"].as<unsigned>();
@@ -193,8 +196,18 @@ main(int argc, char** argv)
 
   // init weights
   vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
-  SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
-  if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &decoder_weights);
+
+  SparseVector<weight_t> lambdas, cumulative_penalties, w_average, fixed;
+  if (cfg.count("input_weights")) {
+    Weights::InitFromFile(cfg["input_weights"].as<string>(), &decoder_weights);
+    if (fix_features) {
+      Weights::InitSparseVector(decoder_weights, &fixed);
+      SparseVector<weight_t>::iterator it = fixed.begin();
+      for (; it != fixed.end(); ++it) {
+        it->second = 1.0;
+      }
+    }
+  }
   Weights::InitSparseVector(decoder_weights, &lambdas);
 
   // meta params for perceptron, SVM
@@ -334,6 +347,8 @@ main(int argc, char** argv)
     if (next || stop) break;
 
     // weights
+    if (fix_features)
+      lambdas.cw_mult(fixed);
     lambdas.init_vector(&decoder_weights);
 
     // getting input
@@ -642,6 +657,8 @@ main(int argc, char** argv)
 
   // write weights to file
   if (select_weights == "best" || keep) {
+    if (fix_features)
+      lambdas.cw_mult(fixed);
     lambdas.init_vector(&decoder_weights);
     string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
     Weights::WriteToFile(w_fn, decoder_weights, true);