How to hstack several sparse matrices (feature matrices)?

Question

I have 3 sparse matrices:

In [39]:

mat1


Out[39]:
(1, 878049)
<1x878049 sparse matrix of type '<type 'numpy.int64'>'
    with 878048 stored elements in Compressed Sparse Row format>

In [37]:

mat2


Out[37]:
(1, 878049)
<1x878049 sparse matrix of type '<type 'numpy.int64'>'
    with 744315 stored elements in Compressed Sparse Row format>

In [35]:

mat3



Out[35]:
(1, 878049)
<1x878049 sparse matrix of type '<type 'numpy.int64'>'
    with 788618 stored elements in Compressed Sparse Row format>

From the documentation, I read that it is possible to hstack, vstack, and concatenate them such type of matrices. So I tried to hstack them:

import numpy as np

matrix1 = np.hstack([[address_feature, dayweek_feature]]).T
matrix2 = np.vstack([[matrix1, pddis_feature]]).T


X = matrix2

However, the dimensions do not match:

In [41]:

X_combined_features.shape

Out[41]:

(2, 1)

Note that I am stacking such matrices since I would like to use them with a scikit-learn classification algorithm. Therefore, How should I hstack a number of different sparse matrices?.

Answer 1

Use the sparse versions of vstack. As general rule you need to use sparse functions and methods, not the numpy ones with similar name. sparse matrices are not subclasses of numpy ndarray.

But, your 3 three matrices do not look sparse. They are 1x878049. One has 878048 nonzero elements - that means just one 0 element.

So you could just as well turned them into dense arrays (with .toarray() or .A) and use np.hstack or np.vstack.

np.hstack([address_feature.A, dayweek_feature.A])

And don't use the double brackets. All concatenate functions take a simple list or tuple of the arrays. And that list can have more than 2 arrays

In [296]: A=sparse.csr_matrix([0,1,2,0,0,1])

In [297]: B=sparse.csr_matrix([0,0,0,1,0,1])

In [298]: C=sparse.csr_matrix([1,0,0,0,1,0])

In [299]: sparse.vstack([A,B,C])
Out[299]: 
<3x6 sparse matrix of type '<class 'numpy.int32'>'
    with 7 stored elements in Compressed Sparse Row format>

In [300]: sparse.vstack([A,B,C]).A
Out[300]: 
array([[0, 1, 2, 0, 0, 1],
       [0, 0, 0, 1, 0, 1],
       [1, 0, 0, 0, 1, 0]], dtype=int32)

In [301]: sparse.hstack([A,B,C]).A
Out[301]: array([[0, 1, 2, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0]], dtype=int32)

In [302]: np.vstack([A.A,B.A,C.A])
Out[302]: 
array([[0, 1, 2, 0, 0, 1],
       [0, 0, 0, 1, 0, 1],
       [1, 0, 0, 0, 1, 0]], dtype=int32)