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
The scipy.sparse.*_matrix has several useful methods, for example, if a is e.g. scipy.sparse.csr_matrix:
a.toarray() - Return a dense ndarray representation of this matrix. (numpy.array, recommended)a.todense() - Return a dense matrix representation of this matrix. (numpy.matrix)Previously, these methods had shorthands (.A for .toarray(), and .M for .todense()), but these have been or will be deprecated as of Scipy v1.14.0.
a, then a.M returns a dense numpy matrix object, and a.A returns a dense numpy array object. Unless you have very good reasons for it (and you probably don't!), stick to numpy arrays, i.e. a.A, and stay away from 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.]])
numpy.matrix(numpy.array(<your_matrix_object>))?numpy.matrix(<your_matrix_object>.toarray()).