Correlation coefficients for sparse matrix in python?

问题

Does anyone know how to compute a correlation matrix from a very large sparse matrix in python? Basically, I am looking for something like numpy.corrcoef that will work on a scipy sparse matrix.

回答1:

You can compute the correlation coefficients fairly straightforwardly from the covariance matrix like this:

import numpy as np
from scipy import sparse

def sparse_corrcoef(A, B=None):

    if B is not None:
        A = sparse.vstack((A, B), format='csr')

    A = A.astype(np.float64)
    n = A.shape[1]

    # Compute the covariance matrix
    rowsum = A.sum(1)
    centering = rowsum.dot(rowsum.T.conjugate()) / n
    C = (A.dot(A.T.conjugate()) - centering) / (n - 1)

    # The correlation coefficients are given by
    # C_{i,j} / sqrt(C_{i} * C_{j})
    d = np.diag(C)
    coeffs = C / np.sqrt(np.outer(d, d))

    return coeffs

Check that it works OK:

# some smallish sparse random matrices
a = sparse.rand(100, 100000, density=0.1, format='csr')
b = sparse.rand(100, 100000, density=0.1, format='csr')

coeffs1 = sparse_corrcoef(a, b)
coeffs2 = np.corrcoef(a.todense(), b.todense())

print(np.allclose(coeffs1, coeffs2))
# True

Be warned:

The amount of memory required for computing the covariance matrix C will be heavily dependent on the sparsity structure of A (and B, if given). For example, if A is an (m, n) matrix containing just a single column of non-zero values then C will be an (n, n) matrix containing all non-zero values. If n is large then this could be very bad news in terms of memory consumption.

回答2:

Just using numpy:

import numpy as np    
C=((A.T*A -(sum(A).T*sum(A)/N))/(N-1)).todense()
V=np.sqrt(np.mat(np.diag(C)).T*np.mat(np.diag(C)))
COV = np.divide(C,V+1e-119)

来源：https://stackoverflow.com/questions/19231268/correlation-coefficients-for-sparse-matrix-in-python

标签

python

numpy

scipy

sparse-matrix

correlation