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train_lda.py
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train_lda.py
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#encoding=utf-8
"""
此脚本根据已经准备好的文本集，训练LDA模型
"""
import logging
import sys
import codecs
from datetime import datetime
reload(sys)
sys.setdefaultencoding('utf-8')
from gensim import corpora, models, similarities
from gensim.models.ldamodel import LdaModel
from logconfig import congifLogger
STOP_WORDS_PATH = 'dataset/chinese-english-stopwords.txt'
# config logging
log = logging.getLogger('Main.train_lda')
congifLogger("train_lda.log", 5)
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO, filename = "train_lda.log")
def load_stop_words():
"""导入所有的中文停用词
"""
stoplist = set()
f = codecs.open(STOP_WORDS_PATH, 'r', 'utf-8')
for line in f:
line = line.strip()
stoplist.add(line)
return stoplist
def load_documents(file_path):
""" 导入所有的文档
Note：此时所有的文档都放在一个文件中
"""
documents = []
f = codecs.open(file_path, 'r', 'utf-8')
for line in f:
line = line.strip()
if line != '':
documents.append(line)
return documents
def is_filtered_word(word):
""" 判断某个单词是否满足token的标准
"""
# 至少是两个字词，同时过滤单个英文字符
if len(word) == 1:
return True
# 过滤数字
flag = True
try:
float(word)
except ValueError:
flag = False
# 如果没有引起ValueError，则是数字
if flag:
return True
return False
def build_dict_corpus(source_text_path, corpus_path, dict_path):
""" 建立字典和corpus，并存储到文件中
`source_text_path` 源文本所在的文件路径
`corpus_path` 存放corpus文件的路径
`dict_path` 字典存储路径
"""
log.info('Loading stop words...')
stoplist = load_stop_words()
log.info('Done.')
log.info('Loading documents...')
documents = load_documents(source_text_path)
log.info('Done.')
log.info('Remove one time words...')
texts = [[word for word in document.lower().split(' ') if word not in stoplist]
for document in documents]
# remove words that appear only once
#print 'Number of documents: %d, then find all tokens...' % len(texts)
#all_tokens = sum(texts, [])
#print 'Number of all tokens: %d' % len(all_tokens)
#print 'finding tokens that appear only once...'
#tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
#print 'Number of token_once: %d, done' % len(tokens_once)
#tokens_once = []
# 去掉只出现一次的词和只包含一个字的词
# 在这里需要保证使用的是python的unicode编码
texts = [[word for word in text if not is_filtered_word(word)]
for text in texts]
log.info('Done.')
# build dict
log.info('Building and saving dict...')
dictionary = corpora.Dictionary(texts)
log.info('Filter extremes...')
# filter extremes,
# see: http://radimrehurek.com/gensim/corpora/dictionary.html#gensim.corpora.dictionary.Dictionary.filter_extremes
dictionary.filter_extremes(no_below = 1, no_above = 0.8, keep_n = None)
dictionary.save(dict_path) # store the dictionary, for future reference
log.info('Done.')
log.info('Building and saving corpus...')
corpus = [dictionary.doc2bow(text) for text in texts]
corpora.MmCorpus.serialize(corpus_path, corpus) # store to disk, for later use
log.info('Done')
def main(argv):
if len(argv) < 4:
print 'python train_lda.py group_id num_topics passes'
sys.exit(1)
group_id = argv[1]
num_topics = int(argv[2])
passes = int(argv[3])
log.info('Prepare corpus for group: %s' % group_id)
base_path = 'tables/' + group_id + '/'
model_base_path = 'ldamodels/' + group_id + '/'
# buid dict and corpus
#now = datetime.now()
indicator = 'title-comment'
source_path = base_path + 'corpus-topic-comment'
corpus_path = model_base_path + 'corpus-'+ indicator + '-' + group_id + '.mm'
dict_path = model_base_path + 'dict-' + indicator + '-' + group_id + '.dict'
log.info('Building the dict...')
build_dict_corpus(source_path, corpus_path, dict_path)
log.info('Loading dict from pre-saved file...')
dictionary = corpora.Dictionary.load(dict_path)
log.info('Done')
#dictionary.save_as_text(base_path + 'text-dict.txt')
log.info('Build a lda model...')
log.info('Loading corpus from pre-saved .mm file...')
mmcorpus = corpora.MmCorpus(corpus_path)
log.info('Done')
log.info('Training lda model...')
model = LdaModel(mmcorpus, num_topics=num_topics, id2word = dictionary, passes = passes)
model_path = model_base_path + indicator + '-' + group_id + '.ldamodel'
model.save(model_path)
log.info('Done.')
model = LdaModel.load(model_path)
model.show_topics(topics=num_topics, topn=10, log=True)
if __name__ == '__main__':
main(sys.argv)