Given an arbitrary numpy array (ndarray), is there a function or a short way to convert it to a scipy.sparse matrix?
I'd like something that works like:
A = numpy.array([0,1,0],[0,0,0],[1,0,0])
S = to_sparse(A, type="csr_matrix")
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3 Answers
I usually do something like
>>> import numpy, scipy.sparse
>>> A = numpy.array([[0,1,0],[0,0,0],[1,0,0]])
>>> Asp = scipy.sparse.csr_matrix(A)
>>> Asp
<3x3 sparse matrix of type '<type 'numpy.int64'>'
with 2 stored elements in Compressed Sparse Row format>
1 Comment
clstaudt
Hm, that was easy, I should have tried that. I didn't expect the constructor to do the conversion. This works for the constructors of the other sparse matrix types, too.
A very useful and pertinent example is in the help!
import scipy.sparse as sp
help(sp)
This gives:
Example 2
---------
Construct a matrix in COO format:
>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
It's also worth noting the various constructors are (again from the help):
1. csc_matrix: Compressed Sparse Column format
2. csr_matrix: Compressed Sparse Row format
3. bsr_matrix: Block Sparse Row format
4. lil_matrix: List of Lists format
5. dok_matrix: Dictionary of Keys format
6. coo_matrix: COOrdinate format (aka IJV, triplet format)
7. dia_matrix: DIAgonal format
To construct a matrix efficiently, use either lil_matrix (recommended) or
dok_matrix. The lil_matrix class supports basic slicing and fancy
indexing with a similar syntax to NumPy arrays.
Your example would be as simple as:
S = sp.csr_matrix(A)
2 Comments
Hooked
@clstaudt I'm not sure what you are looking for,
sp.csr_matrix(A) would construct a csr type matrix from a dense numpy array A, while sp.csc_matrix(A) would construct a type csc matrix, etc... The constructors are the efficient conversion functions you are looking for (though read the caveats in the help).clstaudt
You're right, the constructors do the conversions I was looking for. I expected them to take only arguments that correspond to the way entries are stored, like: A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
Please refer to this answer: https://stackoverflow.com/a/65017153/9979257
In this answer, I have explained how to convert a 2-dimensional NumPy matrix into CSR or CSC format.
