sparse tensor to csr_matrix

from scipy.sparse import csr_matrix

import numpy as np

x = val_data.x

dim = len(x)

print(dim)

edge_index = val_data.edge_index

print(edge_index) #sparse tensor

row = edge_index[0].numpy()

col = edge_index[1].numpy()

edge_num = len(row)

data = np.ones( edge_num )

mtx = csr_matrix((data, (row, col)), shape=(dim, dim))

#print( type(mtx.toarray()), mtx.toarray().shape)

print( mtx.toarray(), type(mtx.toarray()), mtx.toarray().shape) 

let's image 

val_data.x is node features ex) 13x1000

val_data.edge_index is sparse edge index stored by torch tensor

now we want to convert it to csr_matrix

The above code is example for this case.

The print out is like this:

tensor([[ 0,  0,  0,  1,  1,  1,  1,  1,  2,  2,  2,  2,  3,  3,  3,  3,  3,  4,
          4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  6,  6,  6,  6,  6,  7,  7,  7,
          8,  8,  8,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11,
         11, 11, 12, 12, 12, 12],
        [ 1,  3, 10,  0,  2,  3, 10, 11,  1,  3, 11, 12,  0,  1,  2, 11, 12,  5,
          6,  8,  9, 11, 12,  4,  6,  7,  8,  9,  4,  5,  7,  9, 10,  5,  6,  8,
          4,  5,  7,  4,  5,  6, 10, 11,  0,  1,  6,  9, 11,  1,  2,  3,  4,  9,
         10, 12,  2,  3,  4, 11]])
[[0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0.]
 [0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0.]] <class 'numpy.ndarray'> (13, 13)

Thank you

Enjoy Pytorch!

how to merge two csr_matrix, example python source code

let's see the code.

..

from scipy.sparse import csr_matrix

import numpy as np

#first matrix

row = np.array([0, 0, 1, 2, 2, 2])

col = np.array([0, 2, 2, 0, 1, 2])

data = np.array([1, 1, 1, 1, 1, 1])

mtx = csr_matrix((data, (row, col)), shape=(3, 3))

#second matrix

row = np.array([0, 0, 1, 2, 2, 2])

col = np.array([0, 1, 2, 0, 1, 2])

data = np.array([1, 1, 1, 1, 1, 1])

mtx2 = csr_matrix((data, (row, col)), shape=(3, 3))

#merge two matrix

mtx3 = merge_two_csr_mtx(mtx, mtx2)

#check

print('1st\n',mtx)

print('2nd\n',mtx2)

print('merge\n',mtx3)

..

result

1st
   (0, 0) 1
  (0, 2) 1
  (1, 2) 1
  (2, 0) 1
  (2, 1) 1
  (2, 2) 1
2nd
   (0, 0) 1
  (0, 1) 1
  (1, 2) 1
  (2, 0) 1
  (2, 1) 1
  (2, 2) 1
merge
   (0, 0) 2.0
  (0, 1) 1.0
  (0, 2) 1.0
  (1, 2) 2.0
  (2, 0) 2.0
  (2, 1) 2.0
  (2, 2) 2.0

How to convert a scipy csr_matrix back into lists of row, col and data?

refer to code


..
Define matrix & check values

from scipy.sparse import csr_matrix

import numpy as np

row = np.array([0, 0, 1, 2, 2, 2])

col = np.array([0, 1, 2, 0, 1, 2])

data = np.array([1, 1, 1, 1, 1, 1])

mtx2 = csr_matrix((data, (row, col)), shape=(3, 3))

print(mtx2) #matrix print out

print(mtx2.toarray()) #print out by array

>

(0, 0) 1
  (0, 1) 1
  (1, 2) 1
  (2, 0) 1
  (2, 1) 1
  (2, 2) 1

>

[[1 1 0]
 [0 0 1]
 [1 1 1]]

..


...
get back the row, col and data value from matrix

c = mtx2.tocoo()

print(c.row)

print(c.col)

print(c.data)

>

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

...