【发布时间】:2023-04-06 01:05:02
【问题描述】:
使用gensim
,我能够从 LSA 中的一组文档中提取主题,但是如何访问从 LDA 模型生成的主题?
打印lda.print_topics(10)
时,代码出现以下错误,因为print_topics()
返回NoneType
:
Traceback (most recent call last):
File "/home/alvas/workspace/XLINGTOP/xlingtop.py", line 93, in <module>
for top in lda.print_topics(2):
TypeError: 'NoneType' object is not iterable
代码:
from gensim import corpora, models, similarities
from gensim.models import hdpmodel, ldamodel
from itertools import izip
documents = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user perceived response time to error measurement",
"The generation of random binary unordered trees",
"The intersection graph of paths in trees",
"Graph minors IV Widths of trees and well quasi ordering",
"Graph minors A survey"]
# remove common words and tokenize
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in documents]
# remove words that appear only once
all_tokens = sum(texts, [])
tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
texts = [[word for word in text if word not in tokens_once]
for text in texts]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
# I can print out the topics for LSA
lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2)
corpus_lsi = lsi[corpus]
for l,t in izip(corpus_lsi,corpus):
print l,"#",t
print
for top in lsi.print_topics(2):
print top
# I can print out the documents and which is the most probable topics for each doc.
lda = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=50)
corpus_lda = lda[corpus]
for l,t in izip(corpus_lda,corpus):
print l,"#",t
print
# But I am unable to print out the topics, how should i do it?
for top in lda.print_topics(10):
print top
【问题讨论】:
-
您的代码中缺少某些内容,即 corpus_tfidf 计算。请您添加剩余的部分吗?
标签:
python
nlp
lda
topic-modeling
gensim
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