Merge branch 'master' of github.com:pks/cdec-dtrain

1 files changed, 117 insertions, 5 deletions

diff --git a/python/src/sa/rulefactory.pxi b/python/src/sa/rulefactory.pxi
index b10d25dd..7063c2da 100644
--- a/python/src/sa/rulefactory.pxi
+++ b/python/src/sa/rulefactory.pxi
@@ -23,7 +23,17 @@ FeatureContext = namedtuple('FeatureContext',
      'test_sentence',
      'f_text',
      'e_text',
-     'meta'
+     'meta',
+     'online'
+    ])
+
+OnlineFeatureContext = namedtuple('OnlineFeatureContext',
+    ['fcount',
+     'fsample_count',
+     'paircount',
+     'bilex_f',
+     'bilex_e',
+     'bilex_fe'
     ])
 
 cdef int PRECOMPUTE = 0
@@ -264,6 +274,8 @@ cdef class HieroCachingRuleFactory:
     cdef IntList findexes
     cdef IntList findexes1
 
+    cdef bint online
+    cdef samples_f
     cdef phrases_f
     cdef phrases_e
     cdef phrases_fe
@@ -381,6 +393,12 @@ cdef class HieroCachingRuleFactory:
         
         # Online stats 
         
+        # True after data is added
+        self.online = False
+        
+        # Keep track of everything that can be sampled:
+        self.samples_f = defaultdict(int)
+        
         # Phrase counts
         self.phrases_f = defaultdict(int)
         self.phrases_e = defaultdict(int)
@@ -969,6 +987,11 @@ cdef class HieroCachingRuleFactory:
         hit = 0
         reachable_buffer = {}
 
+        # Phrase pairs processed by suffix array extractor.  Do not re-extract
+        # during online extraction.  This is probably the hackiest part of
+        # online grammar extraction.
+        seen_phrases = set()
+        
         # Do not cache between sentences
         self.rules.root = ExtendedTrieNode(phrase_location=PhraseLocation())
 
@@ -1124,7 +1147,12 @@ cdef class HieroCachingRuleFactory:
                                                f, e, count, fcount[f], num_samples,
                                                (k,i+spanlen), locs, input_match, 
                                                fwords, self.fda, self.eda,
-                                               meta))
+                                               meta,
+                                               # Include online stats.  None if none.
+                                               self.online_ctx_lookup(f, e)))
+                                    # Phrase pair processed
+                                    if self.online:
+                                        seen_phrases.add((f, e))
                                     yield Rule(self.category, f, e, scores, alignment)
 
                 if len(phrase) < self.max_length and i+spanlen < len(fwords) and pathlen+1 <= self.max_initial_size:
@@ -1148,7 +1176,29 @@ cdef class HieroCachingRuleFactory:
                         for (i, alt, pathlen) in frontier_nodes:
                             new_frontier.append((k, i, input_match + (i,), alt, pathlen, xnode, phrase +(xcat,), is_shadow_path))
             frontier = new_frontier
-                
+        
+        # Online rule extraction and scoring
+        if self.online:
+            f_syms = tuple(word[0][0] for word in fwords)
+            for (f, lex_i, lex_j) in self.get_f_phrases(f_syms):
+                spanlen = (lex_j - lex_i) + 1
+                if not sym_isvar(f[0]):
+                    spanlen += 1
+                if not sym_isvar(f[1]):
+                    spanlen += 1
+                for e in self.phrases_fe.get(f, ()):
+                    if (f, e) not in seen_phrases:
+                        # Don't add multiple instances of the same phrase here
+                        seen_phrases.add((f, e))
+                        scores = self.scorer.score(FeatureContext(
+                                f, e, 0, 0, 0,
+                                spanlen, None, None, 
+                                fwords, self.fda, self.eda,
+                                meta,
+                                self.online_ctx_lookup(f, e)))
+                        alignment = self.phrases_al[f][e]
+                        yield Rule(self.category, f, e, scores, alignment)
+             
         stop_time = monitor_cpu()
         logger.info("Total time for rule lookup, extraction, and scoring = %f seconds", (stop_time - start_time))
         gc.collect()
@@ -1828,6 +1878,8 @@ cdef class HieroCachingRuleFactory:
     # (Extract rules, update counts)
     def add_instance(self, f_words, e_words, alignment):
 
+        self.online = True
+        
         # Rules extracted from this instance
         # Track span of lexical items (terminals) to make
         # sure we don't extract the same rule for the same
@@ -1970,11 +2022,17 @@ cdef class HieroCachingRuleFactory:
                 continue
             extract(f_i, f_i, f_len + 1, -1, f_i, 0, [], [], False)
         
+        # Update possible phrases (samples)
+        # This could be more efficiently integrated with extraction
+        # at the cost of readability
+        for (f, lex_i, lex_j) in self.get_f_phrases(f_words):
+            self.samples_f[f] += 1
+            
         # Update phrase counts
         for rule in rules:
             (f_ph, e_ph, al) = rule[:3]
             self.phrases_f[f_ph] += 1
-            self.phrases_e[e_ph] += 1 
+            self.phrases_e[e_ph] += 1
             self.phrases_fe[f_ph][e_ph] += 1
             if not self.phrases_al[f_ph][e_ph]:
                 self.phrases_al[f_ph][e_ph] = al
@@ -1987,7 +2045,6 @@ cdef class HieroCachingRuleFactory:
             for e_w in e_words:
                 self.bilex_fe[f_w][e_w] += 1
 
-
     # Create a rule from source, target, non-terminals, and alignments
     def form_rule(self, f_i, e_i, f_span, e_span, nt, al):
     
@@ -2080,10 +2137,65 @@ cdef class HieroCachingRuleFactory:
         for ph in self.phrases_e:
             logger.info(str(ph) + ' ||| ' + str(self.phrases_e[ph]))
         logger.info('FE')
+        self.dump_online_rules()
+
+    def dump_online_rules(self):
         for ph in self.phrases_fe:
             for ph2 in self.phrases_fe[ph]:
                 logger.info(self.fmt_rule(str(ph), str(ph2), self.phrases_al[ph][ph2]) + ' ||| ' + str(self.phrases_fe[ph][ph2]))
+                    
+    # Lookup online stats for phrase pair (f, e).  Return None if no match.
+    # IMPORTANT: use get() to avoid adding items to defaultdict
+    def online_ctx_lookup(self, f, e):
+        if self.online:
+            fcount = self.phrases_f.get(f, 0)
+            fsample_count = self.samples_f.get(f, 0)
+            d = self.phrases_fe.get(f, None)
+            paircount = d.get(e, 0) if d else 0
+            return OnlineFeatureContext(fcount, fsample_count, paircount, self.bilex_f, self.bilex_e, self.bilex_fe)
+        return None
+    
+    # Find all phrases that we might try to extract
+    # (Used for EGivenFCoherent)
+    # Return set of (fphrase, lex_i, lex_j)
+    def get_f_phrases(self, f_words):
+
+        f_len = len(f_words)
+        phrases = set() # (fphrase, lex_i, lex_j)
         
+        def extract(f_i, f_j, lex_i, lex_j, wc, ntc, syms):
+            # Phrase extraction limits
+            if f_j > (f_len - 1) or (f_j - f_i) + 1 > self.max_initial_size:
+                return
+            # Extend with word
+            if wc + ntc < self.max_length:
+                syms.append(f_words[f_j])
+                f = Phrase(syms)
+                new_lex_i = min(lex_i, f_j)
+                new_lex_j = max(lex_j, f_j)
+                phrases.add((f, new_lex_i, new_lex_j))
+                extract(f_i, f_j + 1, new_lex_i, new_lex_j, wc + 1, ntc, syms)
+                syms.pop()
+            # Extend with existing non-terminal
+            if syms and sym_isvar(syms[-1]):
+                # Don't re-extract the same phrase
+                extract(f_i, f_j + 1, lex_i, lex_j, wc, ntc, syms)
+            # Extend with new non-terminal
+            if wc + ntc < self.max_length:
+                if not syms or (ntc < self.max_nonterminals and not sym_isvar(syms[-1])):
+                    syms.append(sym_setindex(self.category, ntc + 1))
+                    f = Phrase(syms)
+                    if wc > 0:
+                        phrases.add((f, lex_i, lex_j))
+                    extract(f_i, f_j + 1, lex_i, lex_j, wc, ntc + 1, syms)
+                    syms.pop()
+            
+        # Try to extract phrases from every f index
+        for f_i from 0 <= f_i < f_len:
+            extract(f_i, f_i, f_len, -1, 0, 0, [])
+
+        return phrases
+    
 # Spans are _inclusive_ on both ends [i, j]
 def span_check(vec, i, j):
     k = i