* Fix multi_word_matches script

examples/multi_word_matches.py

@@ -22,6 +22,10 @@ our pattern set stays very small (exact size depends on the maximum length we're
 looking for, as the query language currently has no quantifiers)
 """
 from __future__ import print_function, unicode_literals, division
+from ast import literal_eval
+from bz2 import BZ2File
+import time
+import math
 
 import plac
 
@@ -30,22 +34,66 @@ from spacy.strings import hash_string
 from spacy.en import English
 from spacy.matcher import Matcher
 
-from spacy.attrs import FLAG63 as U_ENT
+from spacy.attrs import FLAG63 as B_ENT
 from spacy.attrs import FLAG62 as L_ENT
 from spacy.attrs import FLAG61 as I_ENT
-from spacy.attrs import FLAG60 as B_ENT
+
+from spacy.attrs import FLAG60 as B2_ENT
+from spacy.attrs import FLAG59 as B3_ENT
+from spacy.attrs import FLAG58 as B4_ENT
+from spacy.attrs import FLAG57 as B5_ENT
+from spacy.attrs import FLAG56 as B6_ENT
+from spacy.attrs import FLAG55 as B7_ENT
+from spacy.attrs import FLAG54 as B8_ENT
+from spacy.attrs import FLAG53 as B9_ENT
+from spacy.attrs import FLAG52 as B10_ENT
+
+from spacy.attrs import FLAG51 as I3_ENT
+from spacy.attrs import FLAG50 as I4_ENT
+from spacy.attrs import FLAG49 as I5_ENT
+from spacy.attrs import FLAG48 as I6_ENT
+from spacy.attrs import FLAG47 as I7_ENT
+from spacy.attrs import FLAG46 as I8_ENT
+from spacy.attrs import FLAG45 as I9_ENT
+from spacy.attrs import FLAG44 as I10_ENT
+
+from spacy.attrs import FLAG43 as L2_ENT
+from spacy.attrs import FLAG42 as L3_ENT
+from spacy.attrs import FLAG41 as L4_ENT
+from spacy.attrs import FLAG40 as L5_ENT
+from spacy.attrs import FLAG39 as L6_ENT
+from spacy.attrs import FLAG38 as L7_ENT
+from spacy.attrs import FLAG37 as L8_ENT
+from spacy.attrs import FLAG36 as L9_ENT
+from spacy.attrs import FLAG35 as L10_ENT
 
 
 def get_bilou(length):
     if length == 1:
         return [U_ENT]
-    else:
+    elif length == 2:
-        return [B_ENT] + [I_ENT] * (length - 2) + [L_ENT]
+        return [B2_ENT, L2_ENT]
+    elif length == 3:
+        return [B3_ENT, I3_ENT, L3_ENT]
+    elif length == 4:
+        return [B4_ENT, I4_ENT, I4_ENT, L4_ENT]
+    elif length == 5:
+        return [B5_ENT, I5_ENT, I5_ENT, L5_ENT]
+    elif length == 6:
+        return [B6_ENT, I6_ENT, I6_ENT, I6_ENT, I6_ENT, L6_ENT]
+    elif length == 7:
+        return [B7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, L7_ENT]
+    elif length == 8:
+        return [B8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, L8_ENT]
+    elif length == 9:
+        return [B9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, L9_ENT]
+    elif length == 10:
+        return [B10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, L10_ENT]
 
 
 def make_matcher(vocab, max_length):
     abstract_patterns = []
-    for length in range(1, max_length+1):
+    for length in range(2, max_length):
         abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
     return Matcher(vocab, {'Candidate': ('CAND', {}, abstract_patterns)})
 
@@ -66,29 +114,48 @@ def merge_matches(doc, matches):
         doc.merge(start, end, tag, text, 'MWE')
 
 
-def main():
+def read_gazetteer(loc):
+    for line in open(loc):
+        phrase = literal_eval('u' + line.strip())
+        if ' (' in phrase and phrase.endswith(')'):
+            phrase = phrase.split(' (', 1)[0]
+        yield phrase
+
+def read_text(bz2_loc):
+    with BZ2File(bz2_loc) as file_:
+        for line in file_:
+            yield line.decode('utf8')
+
+def main(patterns_loc, text_loc):
     nlp = English(parser=False, tagger=False, entity=False)
     
-    gazetteer = [u'M.I.A.', 'Shiny Happy People', 'James E. Jones']
-    example_text = u'The artist M.I.A. did a cover of Shiny Happy People. People is not an entity.'
     pattern_ids = PreshMap()
-    max_length = 0
+    max_length = 10
-    for pattern_str in gazetteer:
+    i = 0
+    for pattern_str in read_gazetteer(patterns_loc):
         pattern = nlp.tokenizer(pattern_str)
+        if len(pattern) < 2 or len(pattern) >= max_length:
+            continue
         bilou_tags = get_bilou(len(pattern))
         for word, tag in zip(pattern, bilou_tags):
             lexeme = nlp.vocab[word.orth]
             lexeme.set_flag(tag, True)
         pattern_ids[hash_string(pattern.text)] = True
-        max_length = max(max_length, len(pattern))
+        i += 1
+        if i >= 10000001:
+            break
 
     matcher = make_matcher(nlp.vocab, max_length)
 
-    doc = nlp(example_text)
+    t1 = time.time()
+        
+    for text in read_text(text_loc):
+        doc = nlp.tokenizer(text)
         matches = get_matches(matcher, pattern_ids, doc)
         merge_matches(doc, matches)
-    for token in doc:
+    t2 = time.time()
-        print(token.text, token.ent_type_)
+    print('10 ^ %d patterns took %d s' % (round(math.log(i, 10)), t2-t1))
+
     
 
 if __name__ == '__main__':