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Gensim是一个专门针对大规模文本数据进行主题建模和相似性检索的Python库。
MmCorpus是gensim用于高效读写大型稀疏矩阵的一种格式,适用于大数据集。
TF-IDF是一种常见的文本表示方法,通过对词频进行加权以突出重要性较高的词语。
LSI、LDA和RP都是降维或主题提取方法,常用于信息检索、文本分类和聚类任务。
这段代码是使用gensim库生成主题模型的一个脚本,它根据用户提供的语言和方法参数来训练文本数据集,并将训练好的模型保存为文件。以下是核心代码逻辑的分析与解释:
if __name__ == '__main__':
)#!/usr/bin/env python
#
# Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
"""
USAGE: %(program)s LANGUAGE METHOD
Generate topic models for the specified subcorpus. METHOD is currently one \
of 'tfidf', 'lsi', 'lda', 'rp'.
Example: ./gensim_genmodel.py any lsi
"""
import logging
import sys
import os.path
from gensim.corpora import dmlcorpus, MmCorpus
from gensim.models import lsimodel, ldamodel, tfidfmodel, rpmodel
import gensim_build
# internal method parameters
DIM_RP = 300 # dimensionality for random projections
DIM_LSI = 200 # for lantent semantic indexing
DIM_LDA = 100 # for latent dirichlet allocation
if __name__ == '__main__':
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
logging.root.setLevel(level=logging.INFO)
logging.info("running %s", ' '.join(sys.argv))
program = os.path.basename(sys.argv[0])
# check and process input arguments
if len(sys.argv) < 3:
print(globals()['__doc__'] % locals())
sys.exit(1)
language = sys.argv[1]
method = sys.argv[2].strip().lower()
logging.info("loading corpus mappings")
config = dmlcorpus.DmlConfig('%s_%s' % (gensim_build.PREFIX, language),
resultDir=gensim_build.RESULT_DIR, acceptLangs=[language])
logging.info("loading word id mapping from %s", config.resultFile('wordids.txt'))
id2word = dmlcorpus.DmlCorpus.loadDictionary(config.resultFile('wordids.txt'))
logging.info("loaded %i word ids", len(id2word))
corpus = MmCorpus(config.resultFile('bow.mm'))
if method == 'tfidf':
model = tfidfmodel.TfidfModel(corpus, id2word=id2word, normalize=True)
model.save(config.resultFile('model_tfidf.pkl'))
elif method == 'lda':
model = ldamodel.LdaModel(corpus, id2word=id2word, num_topics=DIM_LDA)
model.save(config.resultFile('model_lda.pkl'))
elif method == 'lsi':
# first, transform word counts to tf-idf weights
tfidf = tfidfmodel.TfidfModel(corpus, id2word=id2word, normalize=True)
# then find the transformation from tf-idf to latent space
model = lsimodel.LsiModel(tfidf[corpus], id2word=id2word, num_topics=DIM_LSI)
model.save(config.resultFile('model_lsi.pkl'))
elif method == 'rp':
# first, transform word counts to tf-idf weights
tfidf = tfidfmodel.TfidfModel(corpus, id2word=id2word, normalize=True)
# then find the transformation from tf-idf to latent space
model = rpmodel.RpModel(tfidf[corpus], id2word=id2word, num_topics=DIM_RP)
model.save(config.resultFile('model_rp.pkl'))
else:
raise ValueError('unknown topic extraction method: %s' % repr(method))
MmCorpus.saveCorpus(config.resultFile('%s.mm' % method), model[corpus])
logging.info("finished running %s", program)
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