dtrain: output data

2 files changed, 40 insertions, 28 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index ddd27211..3e9902ab 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -143,10 +143,15 @@ main(int argc, char** argv)
   time_t total_time = 0.;
 
   // output
-  WriteFile raw_out;
-  if (output_raw) raw_out.Init(output_raw_fn);
-  WriteFile updates_out;
-  if (output_updates) updates_out.Init(output_raw_fn);
+  WriteFile out_up, out_raw;
+  if (output_raw) {
+    out_raw.Init(output_raw_fn);
+    *out_raw << setprecision(numeric_limits<double>::digits10+1);
+  }
+  if (output_updates) {
+    out_up.Init(output_updates_fn);
+    *out_up << setprecision(numeric_limits<double>::digits10+1);
+  }
 
 
   for (size_t t = 0; t < T; t++) // T iterations
@@ -220,25 +225,25 @@ main(int argc, char** argv)
     list_sz += observer->effective_size;
 
     if (output_raw)
-      output_sample(sample);
+      output_sample(sample, *out_raw, i);
 
     // update model
     if (!noup) {
 
     SparseVector<weight_t> updates;
     if (structured)
-      num_up += update_structured(sample, updates, margin/*,
-                                  output_updates, updates_out.get()*/); // FIXME
+      num_up += update_structured(sample, updates, margin,
+                                  output_updates, *out_up, i);
     else if (all_pairs)
-      num_up += updates_all(sample, updates, max_up, threshold/*,
-                            output_updates, updates_out.get()*/); // FIXME
+      num_up += updates_all(sample, updates, max_up, threshold,
+                            output_updates, *out_up, i);
     else if (pro)
-      num_up += updates_pro(sample, updates, cut, max_up, threshold/*,
-                            output_updates, updates_out.get()*/); // FIXME
+      num_up += updates_pro(sample, updates, cut, max_up, threshold,
+                            output_updates, *out_up, i);
     else
       num_up += updates_multipartite(sample, updates, cut, margin,
-                                       max_up, threshold, adjust_cut/*,
-                                       output_updates, updates_out.get()*/); // FIXME
+                                     max_up, threshold, adjust_cut,
+                                     output_updates, *out_up, i);
     SparseVector<weight_t> lambdas_copy;
     if (l1_reg)
       lambdas_copy = lambdas;
diff --git a/training/dtrain/update.h b/training/dtrain/update.h
index 30b14771..f6aa9842 100644
--- a/training/dtrain/update.h
+++ b/training/dtrain/update.h
@@ -21,7 +21,8 @@ updates_multipartite(vector<Hyp>* sample,
                      weight_t threshold,
                      bool adjust,
                      bool output=false,
-                     ostream& os=cout)
+                     ostream& os=cout,
+                     size_t id=0)
 {
   size_t up = 0;
   size_t sz = sample->size();
@@ -45,11 +46,11 @@ updates_multipartite(vector<Hyp>* sample,
     for (size_t j = sep_hi; j < sz; j++) {
       Hyp& first=(*sample)[i], second=(*sample)[j];
       if ((first.model-second.model)>margin
-           || (!adjust && first.gold==second.gold)
+           || (first.gold==second.gold)
            || (threshold && (first.gold-second.gold < threshold)))
         continue;
       if (output)
-        os << first.f-second.f << endl;
+        os << id << "\t" << first.f-second.f << endl;
       updates += first.f-second.f;
       if (++up==max_up)
         return up;
@@ -65,11 +66,11 @@ updates_multipartite(vector<Hyp>* sample,
     for (size_t j = sep_lo; j < sz; j++) {
       Hyp& first=(*sample)[i], second=(*sample)[j];
       if ((first.model-second.model)>margin
-           || (!adjust && first.gold==second.gold)
+           || (first.gold==second.gold)
            || (threshold && (first.gold-second.gold < threshold)))
         continue;
       if (output)
-        os << first.f-second.f << endl;
+        os << id << "\t" << first.f-second.f << endl;
       updates += first.f-second.f;
       if (++up==max_up)
         break;
@@ -91,7 +92,8 @@ updates_all(vector<Hyp>* sample,
             size_t max_up,
             weight_t threshold,
             bool output=false,
-            ostream& os=cout)
+            ostream& os=cout,
+            size_t id=0)
 {
   size_t up = 0;
   size_t sz = sample->size();
@@ -102,11 +104,11 @@ updates_all(vector<Hyp>* sample,
   for (size_t i = 0; i < sz-1; i++) {
     for (size_t j = i+1; j < sz; j++) {
       Hyp& first=(*sample)[i], second=(*sample)[j];
-      if (first.gold == second.gold
+      if ((first.gold == second.gold)
            || (threshold && (first.gold-second.gold < threshold)))
         continue;
       if (output)
-        os << first.f-second.f << endl;
+        os << id << "\t" << first.f-second.f << endl;
       updates += first.f-second.f;
       if (++up==max_up)
         break;
@@ -126,7 +128,8 @@ update_structured(vector<Hyp>* sample,
                   SparseVector<weight_t>& updates,
                   weight_t margin,
                   bool output=false,
-                  ostream& os=cout)
+                  ostream& os=cout,
+                  size_t id=0)
 {
   // hope
   sort(sample->begin(), sample->end(), [](Hyp first, Hyp second)
@@ -144,7 +147,7 @@ update_structured(vector<Hyp>* sample,
   if (hope.gold != fear.gold) {
     updates += hope.f - fear.f;
     if (output)
-      os << hope.f << "\t" << fear.f << endl;
+      os << id << "\t" << hope.f << "\t" << fear.f << endl;
 
     return 1;
   }
@@ -170,7 +173,8 @@ updates_pro(vector<Hyp>* sample,
            size_t max_up,
            weight_t threshold,
            bool output=false,
-           ostream& os=cout)
+           ostream& os=cout,
+           size_t id=0)
 {
 
   size_t sz = sample->size(), s;
@@ -198,7 +202,7 @@ updates_pro(vector<Hyp>* sample,
 
   for (auto i: g) {
     if (output)
-      os << i.first->f-i.second->f << endl;
+      os << id << "\t" << i.first->f-i.second->f << endl;
     updates += i.first->f-i.second->f;
   }
 
@@ -212,16 +216,19 @@ updates_pro(vector<Hyp>* sample,
 inline void
 output_sample(vector<Hyp>* sample,
               ostream& os=cout,
+              size_t id=0,
               bool sorted=true)
 {
-  if (sorted)
+  if (sorted) {
     sort(sample->begin(), sample->end(), [](Hyp first, Hyp second)
       {
         return first.gold > second.gold;
       });
+  }
   size_t j = 0;
-  for (auto i: *sample) {
-    os << j << "\t" << i.gold << "\t" << i.model << "\t" << i.f << endl;
+  for (auto k: *sample) {
+    os << id << "\t" << j << "\t" << k.gold << "\t" << k.model
+       << "\t" << k.f << endl;
     j++;
   }
 }