如何使用csr_matrix初始化gensim语料库变量?

问题描述

我有X作为我使用scikit的tfidf矢量化器获得的csr_matrix，而y是一个数组

I have X as a csr_matrix that I obtained using scikit's tfidf vectorizer, and y which is an array

我的计划是使用LDA创建功能，但是，我没找到如何使用X作为csr_matrix初始化gensim的语料库变量的方法.换句话说，我既不想下载gensim文档中所示的语料库，也不想将X转换为密集矩阵，因为它会占用大量内存，并且计算机可能会挂起.

My plan is to create features using LDA, however, I failed to find how to initialize a gensim's corpus variable with X as a csr_matrix. In other words, I don't want to download a corpus as shown in gensim's documentation nor convert X to a dense matrix, since it would consume a lot of memory and the computer could hang.

简而言之，我的问题如下

In short, my questions are the following,

1. 如果我有一个代表整个语料库的csr_matrix(稀疏)，那么如何初始化gensim语料库?
2. 如何使用LDA提取特征?

推荐答案

Gensim具有半隐藏功能，可以为您完成此操作:

Gensim has a semi-well-hidden function that can kind of do this for you:

http://radimrehurek.com/gensim/matutils.html#gensim.matutils.Sparse2Corpus

"gengen.matutils.Sparse2Corpus类(稀疏，documents_columns = True) 将scipy.sparse格式的矩阵转换为流式gensim语料库."

"class gensim.matutils.Sparse2Corpus(sparse, documents_columns=True) Convert a matrix in scipy.sparse format into a streaming gensim corpus."

我已经成功地使用了CountVectorizer提取的语料库，然后将其加载到gensim中.

I've had some success with it using a corpus extracted with CountVectorizer, then loaded into gensim.

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