How do I transform a “SciPy sparse matrix” to a “NumPy matrix”?

Question

I am using a python function called "incidence_matrix(G)", which returns the incident matrix of graph. It is from Networkx package. The problem that I am facing is the return type of this function is "Scipy Sparse Matrix". I need to have the Incident matrix in the format of numpy matrix or array. I was wondering if there is any easy way of doing that or not? Or is there any built-in function that can do this transformation for me or not?

Thanks

Answer 1

The scipy.sparse.*_matrix has several useful methods, for example, if a is eg scipy.sparse.csr_matrix :

• a.toarray() or aA - Return a dense ndarray representation of this matrix. ( numpy.array , recommended)
• a.todense() or aM - Return a dense matrix representation of this matrix. ( numpy.matrix )

Answer 2

The simplest way is to call the todense() method on the data:

In [1]: import networkx as nx

In [2]: G = nx.Graph([(1,2)])

In [3]: nx.incidence_matrix(G)
Out[3]: 
<2x1 sparse matrix of type '<type 'numpy.float64'>'
    with 2 stored elements in Compressed Sparse Column format>

In [4]: nx.incidence_matrix(G).todense()
Out[4]: 
matrix([[ 1.],
        [ 1.]])

In [5]: nx.incidence_matrix(G).todense().A
Out[5]: 
array([[ 1.],
       [ 1.]])

Answer 3

I found that in the case of csr matrices, todense() and toarray() simply wrapped the tuples rather than producing a ndarray formatted version of the data in matrix form. This was unusable for the skmultilearn classifiers I'm training.

I translated it to a lil matrix - a format numpy can parse accurately, and then ran toarray() on that:

sparse.lil_matrix(<my-sparse_matrix>).toarray()