python - 为什么 scipy.sparse.csc_matrix 不保留我的 np.array 的索引顺序？

Question

我正在编写代码以同时有效地从几个大型并行 scipy sparse.csc 矩阵中删除多个列(这意味着所有矩阵都具有相同的 dim，并且所有 nnz 元素都在相同的位置) .我这样做是通过仅索引到我想为一个矩阵保留的列，然后为其他矩阵重用索引和 indptr 列表。但是，当我按列表索引 csc 矩阵时，它会重新排序数据列表，因此我无法重用索引。有没有办法强制 scipy 保持数据列表的原始顺序？为什么仅在按列表索引时才重新排序？

import scipy.sparse
import numpy as np
mat = scipy.sparse.csc_matrix(np.array([[1,0,0,0,2,5], 
                                        [1,0,1,0,0,0], 
                                        [0,0,0,4,0,1],
                                        [0,3,0,1,0,4]]))
print mat[:,3].data

返回数组([4, 1])

print mat[:,[3]].data

返回数组([1, 4])

Answer 1

In [43]: mat = sparse.csc_matrix(np.array([[1,0,0,0,2,5],[1,0,1,0,0,0],[0,0,0,4,
    ...: 0,1],[0,3,0,1,0,4]])) 
    ...:  
    ...:                                                                        
In [44]: mat                                                                    
Out[44]: 
<4x6 sparse matrix of type '<class 'numpy.int64'>'
    with 10 stored elements in Compressed Sparse Column format>
In [45]: mat.data                                                               
Out[45]: array([1, 1, 3, 1, 4, 1, 2, 5, 1, 4], dtype=int64)
In [46]: mat.indices                                                            
Out[46]: array([0, 1, 3, 1, 2, 3, 0, 0, 2, 3], dtype=int32)
In [47]: mat.indptr                                                             
Out[47]: array([ 0,  2,  3,  4,  6,  7, 10], dtype=int32)

标量选择:

In [48]: m1 = mat[:,3]                                                          
In [49]: m1                                                                     
Out[49]: 
<4x1 sparse matrix of type '<class 'numpy.int64'>'
    with 2 stored elements in Compressed Sparse Column format>
In [50]: m1.data                                                                
Out[50]: array([4, 1])
In [51]: m1.indices                                                             
Out[51]: array([2, 3], dtype=int32)
In [52]: m1.indptr                                                              
Out[52]: array([0, 2], dtype=int32)

列表索引:

In [53]: m2 = mat[:,[3]]                                                        
In [54]: m2.data                                                                
Out[54]: array([1, 4], dtype=int64)
In [55]: m2.indices                                                             
Out[55]: array([3, 2], dtype=int32)
In [56]: m2.indptr                                                              
Out[56]: array([0, 2], dtype=int32)

排序:

In [57]: m2.sort_indices()                                                      
In [58]: m2.data                                                                
Out[58]: array([4, 1], dtype=int64)
In [59]: m2.indices                                                             
Out[59]: array([2, 3], dtype=int32)

csc indexing with a list uses matrix multiplication. It constructs an extractor matrix based on the index, and then does the dot multiply. So it's a brand new sparse matrix; not just a subset of the csc data and index attributes.

csc matrices have a method to ensure the indicies values are ordered (within a column). Applying that might help to ensure the arrays are sorted in the same way.