* Fix multi word matcher

examples/multi_word_matches.py

@@ -26,137 +26,71 @@ from ast import literal_eval
 from bz2 import BZ2File
 import time
 import math
+import codecs
 
 import plac
 
 from preshed.maps import PreshMap
+from preshed.counter import PreshCounter
 from spacy.strings import hash_string
 from spacy.en import English
-from spacy.matcher import Matcher
+from spacy.matcher import PhraseMatcher
-
-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 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):
+def read_gazetteer(tokenizer, loc, n=-1):
-    if length == 1:
+    for i, line in enumerate(open(loc)):
-        return [U_ENT]
-    elif length == 2:
-        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(2, max_length):
-        abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
-    return Matcher(vocab, {'Candidate': ('CAND', {}, abstract_patterns)})
-
-
-def get_matches(matcher, pattern_ids, doc):
-    matches = []
-    for label, start, end in matcher(doc):
-        candidate = doc[start : end]
-        if pattern_ids[hash_string(candidate.text)] == True:
-            start = candidate[0].idx
-            end = candidate[-1].idx + len(candidate[-1])
-            matches.append((start, end, candidate.root.tag_, candidate.text))
-    return matches
-
-
-def merge_matches(doc, matches):
-    for start, end, tag, text in matches:
-        doc.merge(start, end, tag, text, 'MWE')
-
-
-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]
+        if i >= n:
+            break
+        phrase = tokenizer(phrase)
+        if len(phrase) >= 2:
             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):
+
+def get_matches(tokenizer, phrases, texts, max_length=6):
+    matcher = PhraseMatcher(tokenizer.vocab, phrases, max_length=max_length)
+    print("Match")
+    for text in texts:
+        doc = tokenizer(text)
+        matches = matcher(doc)
+        for mwe in doc.ents:
+            yield mwe
+
+
+def main(patterns_loc, text_loc, counts_loc, n=10000000):
     nlp = English(parser=False, tagger=False, entity=False)
-    
+    print("Make matcher")
-    pattern_ids = PreshMap()
+    phrases = read_gazetteer(nlp.tokenizer, patterns_loc, n=n)
-    max_length = 10
+    counts = PreshCounter()
-    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
-        i += 1
-        if i >= 10000001:
-            break
-
-    matcher = make_matcher(nlp.vocab, max_length)
-
     t1 = time.time()
-        
+    for mwe in get_matches(nlp.tokenizer, phrases, read_text(text_loc)):
-    for text in read_text(text_loc):
+        counts.inc(hash_string(mwe.text), 1)
-        doc = nlp.tokenizer(text)
-        matches = get_matches(matcher, pattern_ids, doc)
-        merge_matches(doc, matches)
     t2 = time.time()
-    print('10 ^ %d patterns took %d s' % (round(math.log(i, 10)), t2-t1))
+    print("10m tokens in %d s" % (t2 - t1))
     
+    with codecs.open(counts_loc, 'w', 'utf8') as file_:
+        for phrase in read_gazetteer(nlp.tokenizer, patterns_loc, n=n):
+            text = phrase.string
+            key = hash_string(text)
+            count = counts[key]
+            if count != 0:
+                file_.write('%d\t%s\n' % (count, text))
     
 
 if __name__ == '__main__':
+    if False:
+        import cProfile
+        import pstats
+        cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
+        s = pstats.Stats("Profile.prof")
+        s.strip_dirs().sort_stats("time").print_stats()
+    else:
         plac.call(main)