dtrain_net_interface: support for per coordinate learning rates

1 files changed, 40 insertions, 5 deletions

diff --git a/training/dtrain/dtrain_net_interface.h b/training/dtrain/dtrain_net_interface.h
index b201c7a3..720c4c9b 100644
--- a/training/dtrain/dtrain_net_interface.h
+++ b/training/dtrain/dtrain_net_interface.h
@@ -6,13 +6,42 @@
 namespace dtrain
 {
 
+/*
+ * source: http://stackoverflow.com/questions/7724448/\
+           simple-json-string-escape-for-c/33799784#33799784
+ *
+ */
+inline string
+escapeJson(const string& s) {
+  ostringstream o;
+  for (auto c = s.cbegin(); c != s.cend(); c++) {
+    switch (*c) {
+    case '"': o << "\\\""; break;
+    case '\\': o << "\\\\"; break;
+    case '\b': o << "\\b"; break;
+    case '\f': o << "\\f"; break;
+    case '\n': o << "\\n"; break;
+    case '\r': o << "\\r"; break;
+    case '\t': o << "\\t"; break;
+    default:
+      if ('\x00' <= *c && *c <= '\x1f') {
+        o << "\\u"
+          << std::hex << std::setw(4) << std::setfill('0') << (int)*c;
+      } else {
+        o << *c;
+      }
+    }
+  }
+  return o.str();
+}
+
 inline void
-weightsToJson(SparseVector<weight_t>& w, ostringstream& os)
+sparseVectorToJson(SparseVector<weight_t>& w, ostringstream& os)
 {
   vector<string> strs;
   for (typename SparseVector<weight_t>::iterator it=w.begin(),e=w.end(); it!=e; ++it) {
     ostringstream a;
-    a << "\"" << FD::Convert(it->first) << "\":" << it->second;
+    a << "\"" << escapeJson(FD::Convert(it->first)) << "\":" << it->second;
     strs.push_back(a.str());
   }
   for (vector<string>::const_iterator it=strs.begin(); it!=strs.end(); it++) {
@@ -62,10 +91,12 @@ dtrain_net_init(int argc, char** argv, po::variables_map* conf)
     ("margin,m",               po::value<weight_t>()->default_value(0.),       "margin for margin perceptron")
     ("output,o",               po::value<string>()->default_value(""),                   "final weights file")
     ("input_weights,w",        po::value<string>(),                                      "input weights file")
-    ("learning_rate,l",        po::value<weight_t>()->default_value(0.001),                   "learning rate")
-    ("learning_rate_sparse,l", po::value<weight_t>()->default_value(0.00001), "learning rate for sparse features")
+    ("learning_rates,l",       po::value<string>(),                  "pre-defined learning rates per feature")
+    ("learning_rate_R",        po::value<weight_t>(),                    "learning rate for rule id features")
+    ("learning_rate_RB",       po::value<weight_t>(),                "learning rate for rule bigram features")
+    ("learning_rate_Shape",    po::value<weight_t>(),                      "learning rate for shape features")
     ("output_derivation,E",    po::bool_switch()->default_value(false),  "output derivation, not viterbi str")
-    ("output_rules,R",    po::bool_switch()->default_value(false),                        "also output rules")
+    ("output_rules,R",         po::bool_switch()->default_value(false),                   "also output rules")
     ("dense_features,D",       po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV Shape_S01111_T11011 Shape_S11110_T11011 Shape_S11100_T11000 Shape_S01110_T01110 Shape_S01111_T01111 Shape_S01100_T11000 Shape_S10000_T10000 Shape_S11100_T11100 Shape_S11110_T11110 Shape_S11110_T11010 Shape_S01100_T11100 Shape_S01000_T01000 Shape_S01010_T01010 Shape_S01111_T01011 Shape_S01100_T01100 Shape_S01110_T11010 Shape_S11000_T11000 Shape_S11000_T01100 IsSupportedOnline NewRule KnownRule OOVFix"),
                                                                                              "dense features")
     ("debug_output,d",   po::value<string>()->default_value(""),                      "file for debug output");
@@ -84,6 +115,10 @@ dtrain_net_init(int argc, char** argv, po::variables_map* conf)
     cerr << "Missing decoder configuration. Exiting." << endl;
     return false;
   }
+  if (!conf->count("learning_rates")) {
+    cerr << "Missing learning rates. Exiting." << endl;
+    return false;
+  }
   if (!conf->count("addr")) {
     cerr << "No master address given! Exiting." << endl;
     return false;