mirror of https://github.com/explosion/spaCy.git
Format example
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@ -55,15 +55,15 @@ import spacy
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patterns_loc=("Path to gazetteer", "positional", None, str),
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text_loc=("Path to Reddit corpus file", "positional", None, str),
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n=("Number of texts to read", "option", "n", int),
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lang=("Language class to initialise", "option", "l", str))
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def main(patterns_loc, text_loc, n=10000, lang='en'):
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nlp = spacy.blank('en')
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lang=("Language class to initialise", "option", "l", str),
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)
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def main(patterns_loc, text_loc, n=10000, lang="en"):
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nlp = spacy.blank("en")
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nlp.vocab.lex_attr_getters = {}
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phrases = read_gazetteer(nlp.tokenizer, patterns_loc)
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count = 0
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t1 = time.time()
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for ent_id, text in get_matches(nlp.tokenizer, phrases,
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read_text(text_loc, n=n)):
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for ent_id, text in get_matches(nlp.tokenizer, phrases, read_text(text_loc, n=n)):
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count += 1
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t2 = time.time()
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print("%d docs in %.3f s. %d matches" % (n, (t2 - t1), count))
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@ -72,7 +72,7 @@ def main(patterns_loc, text_loc, n=10000, lang='en'):
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def read_gazetteer(tokenizer, loc, n=-1):
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for i, line in enumerate(open(loc)):
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data = ujson.loads(line.strip())
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phrase = tokenizer(data['text'])
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phrase = tokenizer(data["text"])
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for w in phrase:
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_ = tokenizer.vocab[w.text]
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if len(phrase) >= 2:
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@ -83,14 +83,14 @@ def read_text(bz2_loc, n=10000):
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with BZ2File(bz2_loc) as file_:
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for i, line in enumerate(file_):
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data = ujson.loads(line)
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yield data['body']
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yield data["body"]
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if i >= n:
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break
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def get_matches(tokenizer, phrases, texts, max_length=6):
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matcher = PhraseMatcher(tokenizer.vocab, max_length=max_length)
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matcher.add('Phrase', None, *phrases)
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matcher.add("Phrase", None, *phrases)
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for text in texts:
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doc = tokenizer(text)
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for w in doc:
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@ -100,10 +100,11 @@ def get_matches(tokenizer, phrases, texts, max_length=6):
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yield (ent_id, doc[start:end].text)
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if __name__ == '__main__':
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if __name__ == "__main__":
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if False:
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import cProfile
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import pstats
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cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
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s = pstats.Stats("Profile.prof")
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s.strip_dirs().sort_stats("time").print_stats()
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