Concatenate sparse matrices in Python using SciPy/Numpy

问题

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

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)

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>

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

回答1:

You can use the scipy.sparse.hstack:

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

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

来源：https://stackoverflow.com/questions/19710602/concatenate-sparse-matrices-in-python-using-scipy-numpy