From 7079e3685def6f231ecf9f0c3f31b5c03a46d858 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Thu, 17 Mar 2011 22:46:35 -0400
Subject: freeze features, including penalty

---
 training/mpi_online_optimize.cc |  4 +---
 training/online_optimizer.h     | 17 ++++++++++++-----
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
index 1367581a..32033c19 100644
--- a/training/mpi_online_optimize.cc
+++ b/training/mpi_online_optimize.cc
@@ -299,7 +299,7 @@ int main(int argc, char** argv) {
     const string omethod = conf["optimization_method"].as<string>();
     if (omethod == "sgd") {
       const double C = conf["regularization_strength"].as<double>();
-      o.reset(new CumulativeL1OnlineOptimizer(lr, total_corpus_size, C));
+      o.reset(new CumulativeL1OnlineOptimizer(lr, total_corpus_size, C, frozen_fids));
     } else {
       assert(!"fail");
     }
@@ -377,8 +377,6 @@ int main(int argc, char** argv) {
       g.swap(local_grad);
 #endif
       local_grad.clear();
-      for (int i = 0; i < frozen_fids.size(); ++i)
-        g.erase(frozen_fids[i]);
       if (rank == 0) {
         g /= (size_per_proc * size);
         o->UpdateWeights(g, FD::NumFeats(), &x);
diff --git a/training/online_optimizer.h b/training/online_optimizer.h
index 312aabae..61d62a37 100644
--- a/training/online_optimizer.h
+++ b/training/online_optimizer.h
@@ -2,6 +2,7 @@
 #define _ONL_OPTIMIZE_H_
 
 #include <tr1/memory>
+#include <set>
 #include <string>
 #include <cmath>
 #include "sparse_vector.h"
@@ -56,8 +57,12 @@ class OnlineOptimizer {
  public:
   virtual ~OnlineOptimizer();
   OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
-                  size_t batch_size)
-    : N_(batch_size),schedule_(s),k_() {}
+                  size_t batch_size,
+                  const std::vector<int>& frozen_feats = std::vector<int>())
+      : N_(batch_size),schedule_(s),k_() {
+    for (int i = 0; i < frozen_feats.size(); ++i)
+      frozen_.insert(frozen_feats[i]);
+  }
   void ResetEpoch() { k_ = 0; ResetEpochImpl(); }
   void UpdateWeights(const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) {
     ++k_;
@@ -69,6 +74,7 @@ class OnlineOptimizer {
   virtual void ResetEpochImpl();
   virtual void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) = 0;
   const size_t N_; // number of training instances per batch
+  std::set<int> frozen_;  // frozen (non-optimizing) features
 
  private:
   std::tr1::shared_ptr<LearningRateSchedule> schedule_;
@@ -78,8 +84,9 @@ class OnlineOptimizer {
 class CumulativeL1OnlineOptimizer : public OnlineOptimizer {
  public:
   CumulativeL1OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
-                              size_t training_instances, double C) :
-    OnlineOptimizer(s, training_instances), C_(C), u_() {}
+                              size_t training_instances, double C,
+                              const std::vector<int>& frozen) :
+    OnlineOptimizer(s, training_instances, frozen), C_(C), u_() {}
 
  protected:
   void ResetEpochImpl() { u_ = 0; }
@@ -87,7 +94,7 @@ class CumulativeL1OnlineOptimizer : public OnlineOptimizer {
     u_ += eta * C_ / N_;
     (*weights) += eta * approx_g;
     for (int i = 1; i < max_feat; ++i)
-      ApplyPenalty(i, weights);
+      if (frozen_.count(i) == 0) ApplyPenalty(i, weights);
   }
 
  private:
-- 
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