使用SciPy/Numpy在Python中连接稀疏矩阵 [英] Concatenate sparse matrices in Python using SciPy/Numpy

问题描述

使用SciPy/Numpy在Python中连接稀疏矩阵的最有效方法是什么?

What would be the most efficient way to concatenate sparse matrices in Python using SciPy/Numpy?

我在这里使用了以下内容:

Here I used the following:

>>> np.hstack((X, X2))
array([ <49998x70000 sparse matrix of type '<class 'numpy.float64'>'
        with 1135520 stored elements in Compressed Sparse Row format>,
        <49998x70000 sparse matrix of type '<class 'numpy.int64'>'
        with 1135520 stored elements in Compressed Sparse Row format>], 
       dtype=object)

我想在回归中使用两个预测变量，但是当前格式显然不是我想要的格式.是否可能获得以下信息:

I would like to use both predictors in a regression, but the current format is obviously not what I'm looking for. Would it be possible to get the following:

    <49998x1400000 sparse matrix of type '<class 'numpy.float64'>'
     with 2271040 stored elements in Compressed Sparse Row format>

太大，无法转换为深层格式.

It is too large to be converted to a deep format.

推荐答案

您可以使用scipy.sparse.hstack:

from scipy.sparse import hstack
hstack((X, X2))

使用numpy.hstack将创建一个包含两个稀疏矩阵对象的数组.

Using the numpy.hstack will create an array with two sparse matrix objects.

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