How do I transform a "SciPy sparse matrix" to a "NumPy matrix"?

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

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

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()

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.]])