在Scipy中切割稀疏矩阵 - 哪种类型最好？ [英] Slicing Sparse Matrices in Scipy -- Which Types Work Best?

问题描述

SciPy 稀疏矩阵教程非常好 - 但它实际上留下了切片un（der）开发的部分（仍然是大纲形式 - 参见章节：处理稀疏矩阵）。

The SciPy Sparse Matrix tutorial is very good -- but it actually leaves the section on slicing un(der)developed (still in outline form -- see section: "Handling Sparse Matrices").

一旦这个问题得到解答，我将尝试更新教程。

I will try and update the tutorial, once this question is answered.

我有一个大的稀疏矩阵 - 目前采用dok_matrix格式。

I have a large sparse matrix -- currently in dok_matrix format.

import numpy as np
from scipy import sparse
M = sparse.dok_matrix((10**6, 10**6))

对于各种方法，我希望能够切割列和其他我想切片行。理想情况下，我会使用高级索引（即布尔向量， bool_vect ）来切片稀疏矩阵 M - - 如：

For various methods I want to be able to slice columns and for others I want to slice rows. Ideally I would use advanced-indexing (i.e. a boolean vector, bool_vect) with which to slice a sparse matrix M -- as in:

bool_vect = np.arange(10**6)%2  # every even index
out = M[bool_vect,:]            # Want to select every even row

或

out = M[:,bool_vect] # Want to select every even column

首先，dok_matrices不支持这个 - 但我认为如果我第一次转向lil_matrices，通过 sparse.lil_matrix（M）可以（慢慢地）工作

First off, dok_matrices do not support this -- but I think it works (slowly) if I first cast to lil_matrices, via sparse.lil_matrix(M)

据我可以从教程中收集 - 切片列我想要使用CSC并切片行我想切片CSR 。那么这是否意味着我应该通过以下方式投射矩阵 M ：

As far as I can gather from the tutorial -- to slice columns I want to use CSC and to slice rows I want to slice CSR. So does that mean I should cast the matrix M via:

M.tocsc()[:,bool_vect]

或

M.tocsr()[bool_vect,:]

<我有点猜测，因为它，我的代码很慢。知道如何工作的人的任何帮助将不胜感激。提前谢谢。

I am kinda guessing here and my code is slow because of it. Any help from someone who understands how this works would be appreciated. Thanks in advance.

如果事实证明我不应该用布尔数组索引我的矩阵，而是整数（索引）列表 - 这也是我很乐意找到答案。无论哪个效率更高。

If it turns out I should not be indexing my matrix with a boolean array, but rather a list of integers (indices) -- that is also something I would be happy to find out. Whichever is more efficient.

最后 - 这是一个很大的矩阵，如果这可能发生在广播中，那么奖励积分。

Finally -- this is a big matrix, so bonus points if this can happen in place / with broadcasting.

推荐答案

好的，所以我很确定这样做的正确方法是：
如果要切片列，请使用tocsc（）和切片使用整数列表/数组。布尔向量似乎没有使用稀疏矩阵 - 它在numpy中使用ndarrays的方式。这意味着答案是。

Ok, so I'm pretty sure the "right" way to do this is: if you are slicing columns, use tocsc() and slice using a list/array of integers. Boolean vectors does not seem to do the trick with sparse matrices -- the way it does with ndarrays in numpy. Which means the answer is.

indices = np.where(bool_vect)[0]
out1 = M.tocsc()[:,indices]
out2 = M.tocsr()[indices,:]

<但问题是：这是最好的方法吗？这是否到位？

But question: is this the best way? Is this in place?

在实践中，这似乎确实发生了 - 它比以前的尝试快得多（使用lil_matrix）。

In practice this does seem to be happening in place -- and it is much faster than prior attempts (using lil_matrix).

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