在scipy.sparse中将.data属性中的元素设置为零不愉快行为

问题描述

当我将csr_matrix的.data中的值设置为零时，我得到了不愉快的行为.这是一个示例:

I getting unpleasant behavior when I set values in .data of csr_matrix to zero. Here is an example:

from scipy import sparse
a = sparse.csr_matrix([[0,0,2,0], [1,1,0,0],[0,3,0,0]])

输出:

>>> a.A
array([[0, 0, 2, 0],
       [1, 1, 0, 0],
       [0, 3, 0, 0]])
>>> a.data
array([2, 1, 1, 3])
>>> a.data[3] = 0   # setting one element to zero
>>> a.A
array([[0, 0, 2, 0],
       [1, 1, 0, 0],
       [0, 0, 0, 0]])
>>> a.data
array([2, 1, 1, 0]) # however, this zero is still considered part of data
                    # what I would like to see is:
                    # array([2, 1, 1])

>>> a.nnz           # also `nnz` tells me that there 4 non-zero elements
                    # which is incorrect, I would like 3 as an output
4

>>> a.nonzero()     # nonzero method does follow the behavior I expected
(array([0, 1, 1], dtype=int32), array([2, 0, 1], dtype=int32))

在上述情况下的最佳做法是什么?应该避免将.data的元素设置为零吗? .nnz是不可靠的方法来找到零个数吗?

What is the best practice in the above situation? Should setting elements of .data to zero be avoided? Is .nnz unreliable way find number of zeros?

推荐答案

scipy中的稀疏矩阵(至少CSC和CSR)具有 .eliminate_zeros() 方法来处理这种情况.运行

Sparse matrices in scipy (at least CSC and CSR) have an .eliminate_zeros() method to handle this situations. Run

a.eliminate_zeros()

每次您弄乱a.data时，它都应该照顾好它.

every time you mess with a.data, and it should take care of it.

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