How to transform numpy matrix or array to scipy sparse matrix

Question

For SciPy sparse matrix  one can use todense   or toarray   to transform to NumPy matrix or array  What are the functions to do the inverse   I searched  but got no idea what keywords should be the right hit

User · Answer

There are several sparse matrix classes in scipy.

bsr_matrix(arg1[, shape, dtype, copy, blocksize]) Block Sparse Row matrix
coo_matrix(arg1[, shape, dtype, copy]) A sparse matrix in COOrdinate format.
csc_matrix(arg1[, shape, dtype, copy]) Compressed Sparse Column matrix
csr_matrix(arg1[, shape, dtype, copy]) Compressed Sparse Row matrix
dia_matrix(arg1[, shape, dtype, copy]) Sparse matrix with DIAgonal storage
dok_matrix(arg1[, shape, dtype, copy]) Dictionary Of Keys based sparse matrix.
lil_matrix(arg1[, shape, dtype, copy]) Row-based linked list sparse matrix

Any of them can do the conversion.

import numpy as np
from scipy import sparse
a=np.array([[1,0,1],[0,0,1]])
b=sparse.csr_matrix(a)
print(b)

(0, 0)  1
(0, 2)  1
(1, 2)  1

See http://docs.scipy.org/doc/scipy/reference/sparse.html#usage-information .

User · Answer

In Python  the Scipy library can be used to convert the 2-D NumPy matrix into a Sparse matrix  SciPy 2-D sparse matrix package for numeric data is scipy sparse The scipy sparse package provides different Classes to create the following types of Sparse matrices from the 2-dimensional matrix   Block Sparse Row matrix A sparse matrix in COOrdinate format  Compressed Sparse Column matrix Compressed Sparse Row matrix Sparse matrix with DIAgonal storage Dictionary Of Keys based sparse matrix  Row-based list of lists sparse matrix This class provides a base class for all sparse matrices   CSR  Compressed Sparse Row  or CSC  Compressed Sparse Column  formats support efficient access and matrix operations  Example code to Convert Numpy matrix into Compressed Sparse Column CSC  matrix  amp  Compressed Sparse Row  CSR  matrix using Scipy classes  import sys                   Return the size of an object in bytes import numpy as np           To create 2 dimentional matrix from scipy sparse import csr matrix  csc matrix    csr matrix  used to create compressed sparse row matrix from Matrix   csc matrix  used to create compressed sparse column matrix from Matrix  create a 2-D Numpy matrix A   np array   1  0  0  0  0  0                   0  0  2  0  0  1                   0  0  0  2  0  0    print  quot Dense matrix representation   n quot   A  print  quot Memory utilised  bytes    quot   sys getsizeof A   print  quot Type of the object quot   type A    Print the matrix  amp  other details  Dense matrix representation      1 0 0 0 0 0    0 0 2 0 0 1    0 0 0 2 0 0   Memory utilised  bytes    184 Type of the object  lt class  numpy ndarray  gt   Converting Matrix A to the Compressed sparse row matrix representation using csr matrix Class  S   csr matrix A  print  quot Sparse  row  matrix   n quot  S  print  quot Memory utilised  bytes    quot   sys getsizeof S   print  quot Type of the object quot   type S    The output of print statements  Sparse  row  matrix   0  0  1  1  2  2  1  5  1  2  3  2 Memory utilised  bytes   56 Type of the object   lt class  scipy sparse csr csc matrix  gt   Converting Matrix A to Compressed Sparse Column matrix representation using csc matrix Class  S   csc matrix A  print  quot Sparse  column  matrix   n quot  S  print  quot Memory utilised  bytes    quot   sys getsizeof S   print  quot Type of the object quot   type S    The output of print statements  Sparse  column  matrix   0  0  1  1  2  2  2  3  2  1  5  1 Memory utilised  bytes   56 Type of the object   lt class  scipy sparse csc csc matrix  gt   As it can be seen the size of the compressed matrices is 56 bytes and the original matrix size is 184 bytes  For a more detailed explanation and code examples please refer to this article  https   limitlessdatascience wordpress com 2020 11 26 sparse-matrix-in-machine-learning

User · Answer

You can pass a numpy array or matrix as an argument when initializing a sparse matrix  For a CSR matrix  for example  you can do the following    gt  gt  gt  import numpy as np  gt  gt  gt  from scipy import sparse  gt  gt  gt  A   np array   1 2 0   0 0 3   1 0 4     gt  gt  gt  B   np matrix   1 2 0   0 0 3   1 0 4      gt  gt  gt  A array   1  2  0           0  0  3           1  0  4      gt  gt  gt  sA   sparse csr matrix A      Here s the initialization of the sparse matrix   gt  gt  gt  sB   sparse csr matrix B    gt  gt  gt  sA  lt 3x3 sparse matrix of type   lt type  numpy int32  gt           with 5 stored elements in Compressed Sparse Row format gt    gt  gt  gt  print sA    0  0         1    0  1         2    1  2         3    2  0         1    2  2         4

User · Answer

As for the inverse  the function is inv A   but I won t recommend using it  since for huge matrices it is very computationally costly and unstable  Instead  you should use an approximation to the inverse  or if you want to solve Ax   b you don t really need A-1

[python] How to transform numpy.matrix or array to scipy sparse matrix

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