There are two popular kinds of matrices: dense and sparse. Sparse matrices have lots of 'zero' values. In machine learning projects, the learning algorithms require the data to be in-memory. If the data needed for the learning (dataframe) is not in the RAM, then the algorithm does not work. By converting a dense matrix into a sparse matrix it can be made to fit in the RAM.
There are many data structures that can be used to construct a sparse matrix in python. Python Scipy provides the following ways to represent a sparse matrix:
- Block Sparse Row matrix (BSR)
- Coordinate list matrix (COO)
- Compressed Sparse Column matrix (CSC)
- Compressed Sparse Row matrix (CSR)
- Sparse matrix with DIAgonal storage (DIA)
- Dictionary Of Keys based sparse matrix (DOK)
- Row-based linked list sparse matrix (LIL)
The recipe above takes a dense matrix and displays the various formats of sparse matrix that scipy supports.
References: https://docs.scipy.org/doc/scipy/reference/sparse.html
## How to Create A Sparse Matrix
def Kickstarter_Example_2():
print()
print(format('How to Create A Sparse Matrix', '*^50'))
# Load libraries
import numpy as np
from scipy import sparse
# Create a matrix
matrix = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
print()
print("Original Matrix: \n", matrix)
# Create sparse matrices
print()
print("Sparse Matrices: ")
print()
print(sparse.csr_matrix(matrix))
print()
print(sparse.bsr_matrix(matrix))
print()
print(sparse.coo_matrix(matrix))
print()
print(sparse.csc_matrix(matrix))
print()
print(sparse.dia_matrix(matrix))
print()
print(sparse.dok_matrix(matrix))
print()
print(sparse.lil_matrix(matrix))
print()
Kickstarter_Example_2()