votes up 1

efficiency must satisfy 0.0 < efficiency < 1.0

Package:
scipy
github stars 8546
Exception Class:
ValueError

Raise code

    if not (isspmatrix_csr(A) or isspmatrix_csc(A)):
        A = csr_matrix(A)

    if A.nnz == 0:
        return (1,1)

    if not 0 < efficiency < 1.0:
        raise ValueError('efficiency must satisfy 0.0 < efficiency < 1.0')

    high_efficiency = (1.0 + efficiency) / 2.0
    nnz = float(A.nnz)
    M,N = A.shape

    if M % 2 == 0 and N % 2 == 0:
        e22 = nnz / (4 * count_blocks(A,(2,2)))
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Ways to fix

votes up 2 votes down

The value of the efficiency parameter is expected to be between 0 and 1.

Steps to reproduce the error:

$ pipenv install scipy

$ pipenv shell

from scipy.sparse import spfuncs
import scipy
matrix = scipy.sparse.csr_matrix([[0,1],[2,0]])
r,c = spfuncs.estimate_blocksize(matrix,efficiency=1.2)
print(r,c)

Result(Error):

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-11-89b42a8dd3e6> in <module>()
      2 import scipy
      3 matrix = scipy.sparse.csr_matrix([[0,1],[2,0]])
----> 4 r,c = spfuncs.estimate_blocksize(matrix,efficiency=1.2)
      5 print(r,c)


/usr/local/lib/python3.7/dist-packages/scipy/sparse/spfuncs.py in estimate_blocksize(A, efficiency)
     46 
     47     if not 0 < efficiency < 1.0:
---> 48         raise ValueError('efficiency must satisfy 0.0 < efficiency < 1.0')
     49 
     50     high_efficiency = (1.0 + efficiency) / 2.0


ValueError: efficiency must satisfy 0.0 < efficiency < 1.0

Fixed:

from scipy.sparse import spfuncs
import scipy
matrix = scipy.sparse.csr_matrix([[0,1],[2,0]])
r,c = spfuncs.estimate_blocksize(matrix,efficiency=0.7)
print(r,c)

1 1

Sep 21, 2021 kellemnegasi answer
kellemnegasi 30.6k

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