在scipy.sparse中将.data属性中的元素设置为零不愉快行为 [英] Setting elements in .data attribute to zero unpleasant behaivor in scipy.sparse

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

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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