[英]How to get document_topics distribution of all of the document in gensim LDA?
I'm new to python and I need to construct a LDA project. 我是python的新手,我需要构建一个LDA项目。 After doing some preprocessing step, here is my code:
经过一些预处理步骤后,这是我的代码:
dictionary = Dictionary(docs)
corpus = [dictionary.doc2bow(doc) for doc in docs]
from gensim.models import LdaModel
num_topics = 10
chunksize = 2000
passes = 20
iterations = 400
eval_every = None
temp = dictionary[0]
id2word = dictionary.id2token
model = LdaModel(corpus=corpus, id2word=id2word, chunksize=chunksize, \
alpha='auto', eta='auto', \
random_state=42, \
iterations=iterations, num_topics=num_topics, \
passes=passes, eval_every=eval_every)
I want to get a topic distribution of docs, all of the document and get 10 probability of topic distribution, but when I use: 我想获取文档,所有文档的主题分布,并获得10个主题分布的概率,但是当我使用时:
get_document_topics = model.get_document_topics(corpus)
print(get_document_topics)
The output only appear 输出仅出现
<gensim.interfaces.TransformedCorpus object at 0x000001DF28708E10>
How do I get a topic distribution of docs? 如何获得文档的主题分布?
The function get_document_topics
takes an input of a single document in BOW format. 函数
get_document_topics
接受BOW格式的单个文档的输入。 You're calling it on the full corpus (an array of documents) so it returns an iterable object with the scores for each document. 您在完整的语料库(一组文档)上调用它,因此它返回一个带有每个文档分数的可迭代对象。
You have a few options. 您有几种选择。 If you just want one document, run it on the document you want the values for:
如果只需要一个文档,请在需要以下值的文档上运行它:
get_document_topics = model.get_document_topics(corpus[0])
or do the following to get an array of scores for all the documents: 或执行以下操作以获取所有文档的分数数组:
get_document_topics = [model.get_document_topics(item) for item in corpus]
Or directly access each object from your original code: 或直接从原始代码访问每个对象:
get_document_topics = model.get_document_topics(corpus)
print(get_document_topics[0])
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