稀疏矩阵:删除所有元素均为零的行 [英] scipy sparse matrix: remove the rows whose all elements are zero

问题描述

我有一个稀疏矩阵，它是从sklearn tfidfVectorier转换而来的.我相信有些行是全零行.我要删除它们.但是据我所知，现有的内置功能例如nonzero()和exclude_zero()，专注于零项，而不是行.

I have a sparse matrix which is transformed from sklearn tfidfVectorier. I believe that some rows are all-zero rows. I want to remove them. However, as far as I know, the existing built-in functions, e.g. nonzero() and eliminate_zero(), focus on zero entries, rather than rows.

有什么简单的方法可以从稀疏矩阵中删除全零行?

Is there any easy way to remove all-zero rows from a sparse matrix?

示例: 我现在所拥有的(实际上是稀疏格式):

Example: What I have now (actually in sparse format):

[ [0, 0, 0]
  [1, 0, 2]
  [0, 0, 1] ]

我想要得到什么:

[ [1, 0, 2]
  [0, 0, 1] ]

推荐答案

切片+ getnnz()可解决问题:

M = M[M.getnnz(1)>0]

直接在csr_array上工作. 您还可以在不更改格式的情况下删除所有0列:

Works directly on csr_array. You can also remove all 0 columns without changing formats:

M = M[:,M.getnnz(0)>0]

但是，如果要同时删除这两者，则

However if you want to remove both you need

M = M[M.getnnz(1)>0][:,M.getnnz(0)>0] #GOOD

我不确定为什么

M = M[M.getnnz(1)>0, M.getnnz(0)>0] #BAD

不起作用.

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