I'm reading a weighted egdelist / numpy array like:
0 1 1
0 2 1
1 2 1
1 0 1
2 1 4
where the columns are 'User1','User2','Weight'. I'd like to perform a DFS algorithm with scipy.sparse.csgraph.depth_first_tree
, which requires a N x N matrix as input. How can I convert the previous list into a square matrix as:
0 1 1
1 0 1
0 4 0
within numpy or scipy?
Thanks for your help.
EDIT:
I've been working with a huge (150 million nodes) network, so I'm looking for a memory efficient way to do that.
You could use a memory-efficient scipy.sparse matrix:
import numpy as np
import scipy.sparse as sparse
arr = np.array([[0, 1, 1],
[0, 2, 1],
[1, 2, 1],
[1, 0, 1],
[2, 1, 4]])
shape = tuple(arr.max(axis=0)[:2]+1)
coo = sparse.coo_matrix((arr[:, 2], (arr[:, 0], arr[:, 1])), shape=shape,
dtype=arr.dtype)
print(repr(coo))
# <3x3 sparse matrix of type '<type 'numpy.int64'>'
# with 5 stored elements in COOrdinate format>
To convert the sparse matrix to a dense numpy array, you could use todense
:
print(coo.todense())
# [[0 1 1]
# [1 0 1]
# [0 4 0]]
Try something like the following:
import numpy as np
import scipy.sparse as sps
A = np.array([[0, 1, 1],[0, 2, 1],[1, 2, 1],[1, 0, 1],[2, 1, 4]])
i, j, weight = A[:,0], A[:,1], A[:,2]
# find the dimension of the square matrix
dim = max(len(set(i)), len(set(j)))
B = sps.lil_matrix((dim, dim))
for i,j,w in zip(i,j,weight):
B[i,j] = w
print B.todense()
>>>
[[ 0. 1. 1.]
[ 1. 0. 1.]
[ 0. 4. 0.]]
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