Sparse Matrix: ValueError: matrix type must be 'f', 'd', 'F', or 'D'

Question

I want to do SVD on a sparse matrix by using scipy:

from svd import compute_svd
print("The size of raw matrix: "+str(len(raw_matrix))+" * "+str(len(raw_matrix[0])))

from scipy.sparse import dok_matrix
dok = dok_matrix(raw_matrix)

matrix = compute_svd( dok )

The function compute_svd is my customized module like this:

def compute_svd( matrix ):
    from scipy.sparse import linalg
    from scipy import dot, mat
    # e.g., matrix = [[2,1,0,0], [4,3,0,0]]
#    matrix = mat( matrix );
#    print "Original matrix:"
#    print matrix
    U, s, V = linalg.svds( matrix )
    print "U:"
    print U
    print "sigma:"
    print s
    print "VT:"
    print V
    dimensions = 1
    rows,cols = matrix.shape
    #Dimension reduction, build SIGMA'
    for index in xrange(dimensions, rows):
        s[index]=0
    print "reduced sigma:"
    print s
    #Reconstruct MATRIX'
#    from scipy import dot
    reconstructedMatrix= dot(dot(U,linalg.diagsvd(s,len(matrix),len(V))),V)
    #Print transform
    print "reconstructed:"
    print reconstructedMatrix

    return reconstructedMatrix

I get an exception:

Traceback (most recent call last):
  File "D:\workspace\PyQuEST\src\Practice\baseline_lsi.py", line 96, in <module>
    matrix = compute_svd( dok )
  File "D:\workspace\PyQuEST\src\Practice\svd.py", line 13, in compute_svd
    U, s, V = linalg.svds( matrix )
  File "D:\Program\Python26\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1596, in svds
    eigvals, eigvec = eigensolver(XH_X, k=k, tol=tol ** 2)
  File "D:\Program\Python26\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1541, in eigsh
    ncv, v0, maxiter, which, tol)
  File "D:\Program\Python26\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 519, in __init__
    ncv, v0, maxiter, which, tol)
  File "D:\Program\Python26\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 326, in __init__
    raise ValueError("matrix type must be 'f', 'd', 'F', or 'D'")
ValueError: matrix type must be 'f', 'd', 'F', or 'D'

This is my first time to do this. How should I fix it? Any ideas? Thank you!

Answer 1

you have to use float or doubles. you seem to be using unsupported matrix type DOK of ints?.

sparse svd: http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.svds.html

Answer 2

Adding to Anycorn's answer, yes you need to upcast your matrix to float or double. This can be done using the function: asfptype() from scipy.sparse.coo_matrix

Add this line to upcast it before you call linalg.svds:

matrix = matrix.asfptype()
U, s, V = linalg.svds( matrix )