对于稀疏的科学矩阵，将行设置为零的最有效方法是什么? [英] What is most efficient way of setting row to zeros for a sparse scipy matrix?

问题描述

我正在尝试将以下MATLAB代码转换为Python，并且在找到可以在合理的时间内工作的解决方案时遇到了麻烦.

I'm trying to convert the following MATLAB code to Python and am having trouble finding a solution that works in any reasonable amount of time.

M = diag(sum(a)) - a;
where = vertcat(in, out);
M(where,:) = 0;
M(where,where) = 1;

在这里，a是一个稀疏矩阵，而在这里是一个向量(按in/out排列).我使用Python的解决方案是:

Here, a is a sparse matrix and where is a vector (as are in/out). The solution I have using Python is:

M = scipy.sparse.diags([degs], [0]) - A
where = numpy.hstack((inVs, outVs)).astype(int)
M = scipy.sparse.lil_matrix(M)
M[where, :] = 0  # This is the slowest line
M[where, where] = 1
M = scipy.sparse.csc_matrix(M)

但是，由于A为334863x334863，这大约需要三分钟.如果有人对如何加快此速度有任何建议，请贡献他们！为了进行比较，MATLAB快速地执行了相同的步骤.

But since A is 334863x334863, this takes like three minutes. If anyone has any suggestions on how to make this faster, please contribute them! For comparison, MATLAB does this same step imperceptibly fast.

谢谢！

推荐答案

我用于类似任务的解决方案归因于@seberg 并且不转换为lil格式:

The solution I use for similar task attributes to @seberg and do not convert to lil format:

import scipy.sparse
import numpy
import time

def csr_row_set_nz_to_val(csr, row, value=0):
    """Set all nonzero elements (elements currently in the sparsity pattern)
    to the given value. Useful to set to 0 mostly.
    """
    if not isinstance(csr, scipy.sparse.csr_matrix):
        raise ValueError('Matrix given must be of CSR format.')
    csr.data[csr.indptr[row]:csr.indptr[row+1]] = value

def csr_rows_set_nz_to_val(csr, rows, value=0):
    for row in rows:
        csr_row_set_nz_to_val(csr, row)
    if value == 0:
        csr.eliminate_zeros()

将评估与时机结合起来

def evaluate(size):
    degs = [1]*size
    inVs = list(xrange(1, size, size/25))
    outVs = list(xrange(5, size, size/25))
    where = numpy.hstack((inVs, outVs)).astype(int)
    start_time = time.time()
    A = scipy.sparse.csc_matrix((size, size))
    M = scipy.sparse.diags([degs], [0]) - A
    csr_rows_set_nz_to_val(M, where)
    return time.time()-start_time

并测试其性能:

>>> print 'elapsed %.5f seconds' % evaluate(334863)
elapsed 0.53054 seconds

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