-
-
Save hubgit/66603ae1df5863e79629 to your computer and use it in GitHub Desktop.
Create Doc2Vec using Elasticsearch (while processing the data in parallel)
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from elasticsearch.helpers import scan | |
from elasticsearch import Elasticsearch | |
from multiprocessing import Pool | |
import gensim | |
import logging | |
import nltk | |
import os | |
import re | |
import string | |
import unicodedata | |
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR) | |
logging.getLogger('gensim').setLevel(logging.INFO) | |
tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+') | |
stop_words = set(nltk.corpus.stopwords.words('english')) | |
es = Elasticsearch(['localhost']) | |
def create_model(): | |
model = gensim.models.Doc2Vec(size=300, window=8, min_count=10, workers=4) | |
model.build_vocab(sentence_generator()) | |
alpha, min_alpha, passes = (0.025, 0.001, 10) | |
alpha_delta = (alpha - min_alpha) / passes | |
for epoch in range(0, passes): | |
model.alpha, model.min_alpha = alpha, alpha | |
model.train(sentence_generator()) | |
alpha -= alpha_delta | |
print('Finished epoch {}'.format(epoch)) | |
model.save('doc2vec_model_300_10') | |
def get_sentences(document): | |
final = [] | |
if not document.get('fields'): | |
return final | |
abstracts = ' '.join(document['fields']['articles.abstractText']) | |
sentences = nltk.sent_tokenize(abstracts) | |
sentences = [tokenize(sent) for sent in sentences] | |
for sentence_num, sentence in enumerate(sentences): | |
if len(sentence) == 0: | |
continue | |
final.append(gensim.models.doc2vec.TaggedDocument( | |
words=sentence, | |
tags=['{}_{}'.format(document['_id'], sentence_num)] | |
)) | |
return final | |
def sentence_generator(): | |
documents = scan( | |
es, index='pmc', doc_type='recentauthor', | |
scroll='30m', fields='articles.abstractText' | |
) | |
p = Pool(processes=4) | |
for sentences in p.imap(get_sentences, documents): | |
for sentence in sentences: | |
yield sentence | |
def not_stopword(token): | |
return token not in stop_words | |
num_replace = re.compile(r'[0-9]+') | |
def tokenize(sentence): | |
token_list = [] | |
for token in tokenizer.tokenize(sentence): | |
nkfd_form = unicodedata.normalize('NFKD', token) | |
only_ascii = nkfd_form.encode('ASCII', 'ignore').decode('ascii') | |
final = num_replace.sub('DDD', only_ascii) | |
token_list.append(final.strip().lower()) | |
return filter(not_stopword, token_list) | |
if __name__ == '__main__': | |
create_model() | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment