Fixed up evaluation pipeline. Should work now ...

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@183 ec762483-ff6d-05da-a07a-a48fb63a330f

2 files changed, 75 insertions, 42 deletions

diff --git a/extools/featurize_grammar.cc b/extools/featurize_grammar.cc
index 771948ce..0d054626 100644
--- a/extools/featurize_grammar.cc
+++ b/extools/featurize_grammar.cc
@@ -390,8 +390,10 @@ struct LogRuleCount : public FeatureExtractor {
 // i.e. the prob Hiero would assign.
 struct XFeatures: public FeatureExtractor {
   XFeatures() :
-    fid_fe(FD::Convert("XFE")),
-    fid_ef(FD::Convert("XEF")),
+    fid_xfe(FD::Convert("XFE")),
+    fid_xef(FD::Convert("XEF")),
+    fid_labelledfe(FD::Convert("LabelledFE")),
+    fid_labelledef(FD::Convert("LabelledEF")),
     kCFE(FD::Convert("CFE")) {}
   virtual void ObserveFilteredRule(const WordID /*lhs*/,
                                    const vector<WordID>& src,
@@ -399,7 +401,11 @@ struct XFeatures: public FeatureExtractor {
     RuleTuple r(-1, src, trg);
     map_rule(r);
     rule_counts.inc(r, 0);
+
+    normalise_string(r.source());
     source_counts.inc(r.source(), 0);
+
+    normalise_string(r.target());
     target_counts.inc(r.target(), 0);
   }
 
@@ -413,7 +419,11 @@ struct XFeatures: public FeatureExtractor {
 //    cerr << "   ObserveUnfilteredRule() in:" << r << " " << hash_value(r) << endl;
     map_rule(r);
     rule_counts.inc_if_exists(r, info.counts.value(kCFE));
+
+    normalise_string(r.source());
     source_counts.inc_if_exists(r.source(), info.counts.value(kCFE));
+
+    normalise_string(r.target());
     target_counts.inc_if_exists(r.target(), info.counts.value(kCFE));
 //    cerr << "   ObserveUnfilteredRule() inc: " << r << " " << hash_value(r) << " " << info.counts.value(kCFE) << " to " << rule_counts(r) << endl;
   }
@@ -421,15 +431,22 @@ struct XFeatures: public FeatureExtractor {
   virtual void ExtractFeatures(const WordID /*lhs*/,
                                const vector<WordID>& src,
                                const vector<WordID>& trg,
-                               const RuleStatistics& /*info*/,
+                               const RuleStatistics& info,
                                SparseVector<float>* result) const {
     RuleTuple r(-1, src, trg);
     map_rule(r);
-    //result->set_value(fid_fe, log(target_counts(r.target())) - log(rule_counts(r)));
-    //result->set_value(fid_ef, log(source_counts(r.source())) - log(rule_counts(r)));
-    result->set_value(fid_ef, target_counts(r.target()));
-    result->set_value(fid_fe, rule_counts(r));
-    //result->set_value(fid_fe, (source_counts(r.source())));
+    //result->set_value(fid_fe, rule_counts(r));
+    double l_r_freq = log(rule_counts(r));
+
+    normalise_string(r.target());
+    result->set_value(fid_xfe, log(target_counts(r.target())) - l_r_freq);
+    result->set_value(fid_labelledfe, log(target_counts(r.target())) - log(info.counts.value(kCFE)));
+    //result->set_value(fid_labelledfe, target_counts(r.target()));
+
+    normalise_string(r.source());
+    result->set_value(fid_xef, log(source_counts(r.source())) - l_r_freq);
+    result->set_value(fid_labelledef, log(source_counts(r.source())) - log(info.counts.value(kCFE)));
+    //result->set_value(fid_labelledef, source_counts(r.source()));
   }
 
   void map_rule(RuleTuple& r) const {
@@ -444,7 +461,14 @@ struct XFeatures: public FeatureExtractor {
     }
   }
 
-  const int fid_fe, fid_ef;
+  void normalise_string(vector<WordID>& r) const {
+    vector<WordID> indexes;
+    for (vector<WordID>::iterator it = r.begin(); it != r.end(); ++it)
+      if (validate_non_terminal(TD::Convert(*it))) *it = -1;
+  }
+
+  const int fid_xfe, fid_xef;
+  const int fid_labelledfe, fid_labelledef;
   const int kCFE;
   RuleFreqCount rule_counts;
   FreqCount< vector<WordID> > source_counts, target_counts;
@@ -452,69 +476,71 @@ struct XFeatures: public FeatureExtractor {
 
 struct LabelledRuleConditionals: public FeatureExtractor {
   LabelledRuleConditionals() :
+    fid_count(FD::Convert("TCount")),
+    fid_e(FD::Convert("TCountE")),
+    fid_f(FD::Convert("TCountF")),
     fid_fe(FD::Convert("TLabelledFE")),
     fid_ef(FD::Convert("TLabelledEF")),
     kCFE(FD::Convert("CFE")) {}
-  virtual void ObserveFilteredRule(const WordID /*lhs*/,
+  virtual void ObserveFilteredRule(const WordID lhs,
                                    const vector<WordID>& src,
                                    const vector<WordID>& trg) {
-    RuleTuple r(-1, src, trg);
+    RuleTuple r(lhs, src, trg);
     rule_counts.inc(r, 0);
-    cerr << "   ObservefilteredRule() inc: " << r << " " << hash_value(r) << endl;
-//    map_rule(r);
+    //cerr << "   ObservefilteredRule() inc: " << r << " " << hash_value(r) << endl;
+    normalise_string(r.source());
     source_counts.inc(r.source(), 0);
+
+    normalise_string(r.target());
     target_counts.inc(r.target(), 0);
   }
 
   // compute statistics over keys, the same lhs-src-trg tuple may be seen
   // more than once
-  virtual void ObserveUnfilteredRule(const WordID /*lhs*/,
+  virtual void ObserveUnfilteredRule(const WordID lhs,
                                      const vector<WordID>& src,
                                      const vector<WordID>& trg,
                                      const RuleStatistics& info) {
-    RuleTuple r(-1, src, trg);
-    //cerr << "   ObserveUnfilteredRule() in:" << r << " " << hash_value(r) << endl;
+    RuleTuple r(lhs, src, trg);
     rule_counts.inc_if_exists(r, info.counts.value(kCFE));
-    cerr << "   ObserveUnfilteredRule() inc_if_exists: " << r << " " << hash_value(r) << " " << info.counts.value(kCFE) << " to " << rule_counts(r) << endl;
-//    map_rule(r);
+    //cerr << "   ObserveUnfilteredRule() inc_if_exists: " << r << " " << hash_value(r) << " " << info.counts.value(kCFE) << " to " << rule_counts(r) << endl;
+    normalise_string(r.source());
     source_counts.inc_if_exists(r.source(), info.counts.value(kCFE));
+
+    normalise_string(r.target());
     target_counts.inc_if_exists(r.target(), info.counts.value(kCFE));
   }
 
-  virtual void ExtractFeatures(const WordID /*lhs*/,
+  virtual void ExtractFeatures(const WordID lhs,
                                const vector<WordID>& src,
                                const vector<WordID>& trg,
-                               const RuleStatistics& info,
+                               const RuleStatistics& /*info*/,
                                SparseVector<float>* result) const {
-    RuleTuple r(-1, src, trg);
-    //cerr << "   ExtractFeatures() in:" << " " << r.m_hash << endl;
-    int r_freq = rule_counts(r);
-    cerr << "   ExtractFeatures() count: " << r << " " << hash_value(r) << " " << info.counts.value(kCFE) << " | " << rule_counts(r) << endl;
-    assert(r_freq == info.counts.value(kCFE));
+    RuleTuple r(lhs, src, trg);
+    double l_r_freq = log(rule_counts(r));
+    //result->set_value(fid_count, rule_counts(r));
+    //cerr << "   ExtractFeatures() count: " << r << " " << info.counts.value(kCFE) << " | " << rule_counts(r) << endl;
+    //assert(l_r_freq == log(info.counts.value(kCFE)));
     //cerr << "   ExtractFeatures() after:" << " " << r.hash << endl;
     //cerr << "   ExtractFeatures() in:" << r << " " << r_freq << " " << hash_value(r) << endl;
     //cerr << "   ExtractFeatures() in:" << r << " " << r_freq << endl;
-//    map_rule(r);
-    //result->set_value(fid_fe, log(target_counts(r.target())) - log(r_freq));
-    //result->set_value(fid_ef, log(source_counts(r.source())) - log(r_freq));
-    result->set_value(fid_ef, target_counts(r.target()));
-    result->set_value(fid_fe, r_freq);
-    //result->set_value(fid_fe, (source_counts(r.source())));
+    normalise_string(r.target());
+    result->set_value(fid_fe, log(target_counts(r.target())) - l_r_freq);
+    normalise_string(r.source());
+    result->set_value(fid_ef, log(source_counts(r.source())) - l_r_freq);
+
+    //result->set_value(fid_e, target_counts(r.target()));
+    //result->set_value(fid_f, source_counts(r.source()));
   }
 
-  void map_rule(RuleTuple& r) const {
-    vector<WordID> indexes; int i=0;
-    for (vector<WordID>::iterator it = r.target().begin(); it != r.target().end(); ++it) {
-      if (validate_non_terminal(TD::Convert(*it)))
-        indexes.push_back(*it);
-    }
-    for (vector<WordID>::iterator it = r.source().begin(); it != r.source().end(); ++it) {
-      if (validate_non_terminal(TD::Convert(*it)))
-        *it = indexes.at(i++);
-    }
+  void normalise_string(vector<WordID>& r) const {
+    vector<WordID> indexes;
+    for (vector<WordID>::iterator it = r.begin(); it != r.end(); ++it)
+      if (validate_non_terminal(TD::Convert(*it))) *it = -1;
   }
 
   const int fid_fe, fid_ef;
+  const int fid_count, fid_e, fid_f;
   const int kCFE;
   RuleFreqCount rule_counts;
   FreqCount< vector<WordID> > source_counts, target_counts;
diff --git a/gi/pipeline/evaluation-pipeline.pl b/gi/pipeline/evaluation-pipeline.pl
index 8414308d..619e2699 100755
--- a/gi/pipeline/evaluation-pipeline.pl
+++ b/gi/pipeline/evaluation-pipeline.pl
@@ -10,6 +10,9 @@ my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR
 # the key in this map is the extractor name, the value is a list of the extracted features
 my $feat_map = {
   "LogRuleCount" => [ "LogRuleCount", "SingletonRule" ] ,
+#  "XFeatures" => [ "XFE","XEF" ] ,
+  "XFeatures" => [ "XFE","XEF","LabelledEF","LabelledFE"] ,
+  "LabelledRuleConditionals" => [ "LabelledFE","LabelledEF" ] ,
   "LexProb" => [ "LexE2F", "LexF2E" ] ,
 };
 
@@ -24,11 +27,15 @@ my %init_weights = qw(
   LogECount -0.129853
   LogFCount -0.194037
   LogRuleCount 0.256706
+  XFE -0.256706
+  XEF -0.256706
+  LabelledFE -0.256706
+  LabelledEF -0.256706
   PassThrough -0.9304905
   SingletonE -3.04161
   SingletonF 0.0714027
   SingletonRule -0.889377
-  WordPenalty -7.99495
+  WordPenalty -1.99495
 );