How to implement Circular Permutation (left and right shift) of CSR_Matrix in Scipy Python Sparse Matrices?

Question

I am using Scipy sparse matrix `csr_matrix` to be used as context vectors in word-context vectors. My `csr_matrix` is a `(1, 300)` shape so it is a 1-dimensional vector. I need to use permutation (circular right shift or circular left shift) on the sparse vector (for showing left context and right context). example: i have `[1, 2, 3, 4]` and i want to create right and left permutations as follow: right permutation: `[4, 1, 2, 3]` left permutation: `[2, 3, 4, 1]` In csr matrices i can't access to column indices so i can not just change the column indices. Is there any efficient high performance solution for row permutations in `csr_matrix` or am i missing something? runnable code: ``` from scipy.sparse import csr_matrix rows = [0, 0, 0] columns = [100, 47, 150] data = [-1, +1, -1] contextMatrix = csr_matrix( (data,(rows, columns)), shape=(1, 300) ) ``` it means that i have a 300-column vector whose columns 100, 47, 150 all from row 0 are non-zero valued and their value is in data list…

Accepted Answer

You can access and alter the `data` and `indices` attributes of your CSR matrix, which are stored as NumPy arrays.

http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix

So using your code and following the suggestion in the comments you could do this:

```
from scipy.sparse import csr_matrix
rows = [0, 0, 0]
columns = [100, 47, 150]
data = [-1, +1, -1]
m = csr_matrix( (data,(rows, columns)), shape=(1, 300) )

indices = m.indices

# right permutation
m.indices = (indices + 1) % m.shape[1]

# left permutation
m.indices = (indices - 1) % m.shape[1]
```

How to implement Circular Permutation (left and right shift) of CSR_Matrix in Scipy Python Sparse Matrices?

Solution

Problem