如何使用 sklearn 计算词-词共现矩阵? [英] How do I calculate a word-word co-occurrence matrix with sklearn?

问题描述

我正在 sklearn 中寻找一个模块，它可以让您导出词-词共现矩阵.

I am looking for a module in sklearn that lets you derive the word-word co-occurrence matrix.

我可以得到文档-术语矩阵，但不知道如何获得共现的词-词矩阵.

I can get the document-term matrix but not sure how to go about obtaining a word-word matrix of co-ocurrences.

推荐答案

这是我在 scikit-learn 中使用 CountVectorizer 的示例解决方案.并参考此帖子，您可以简单地使用矩阵乘法得到词词共现矩阵.

Here is my example solution using CountVectorizer in scikit-learn. And referring to this post, you can simply use matrix multiplication to get word-word co-occurrence matrix.

from sklearn.feature_extraction.text import CountVectorizer
docs = ['this this this book',
        'this cat good',
        'cat good shit']
count_model = CountVectorizer(ngram_range=(1,1)) # default unigram model
X = count_model.fit_transform(docs)
# X[X > 0] = 1 # run this line if you don't want extra within-text cooccurence (see below)
Xc = (X.T * X) # this is co-occurrence matrix in sparse csr format
Xc.setdiag(0) # sometimes you want to fill same word cooccurence to 0
print(Xc.todense()) # print out matrix in dense format

也可以参考count_model、

count_model.vocabulary_

或者，如果您想通过对角线分量进行归一化(参考上一篇文章中的回答).

Or, if you want to normalize by diagonal component (referred to answer in previous post).

import scipy.sparse as sp
Xc = (X.T * X)
g = sp.diags(1./Xc.diagonal())
Xc_norm = g * Xc # normalized co-occurence matrix

额外要注意@Federico Caccia的回答，如果您不希望自己的文本中出现虚假的共现，请将出现次数设置为大于 1 到 1，例如

Extra to note @Federico Caccia answer, if you don't want co-occurrence that are spurious from the own text, set occurrence that is greater that 1 to 1 e.g.

X[X > 0] = 1 # do this line first before computing cooccurrence
Xc = (X.T * X)
...

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