I have a big csr_matrix and I want to add over rows and obtain a new csr_matrix with the same number of columns but reduced number of rows. (Context: The matrix is a document-term matrix obtained from sklearn CountVectorizer and I want to be able to quickly combine documents according to codes associated with these documents)
For a minimal example, this is my matrix:
import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse import vstack
row = np.array([0, 4, 1, 3, 2])
col = np.array([0, 2, 2, 0, 1])
dat = np.array([1, 2, 3, 4, 5])
A = csr_matrix((dat, (row, col)), shape=(5, 5))
print A.toarray()
[[1 0 0 0 0]
[0 0 3 0 0]
[0 5 0 0 0]
[4 0 0 0 0]
[0 0 2 0 0]]
No let's say I want a new matrix B
in which rows (1, 4) and (2, 3, 5) are combined by summing them, which would look something like this:
[[5 0 0 0 0]
[0 5 5 0 0]]
And should be again in sparse format (because the real data I'm working with is large). I tried to sum over slices of the matrix and then stack it:
idx1 = [1, 4]
idx2 = [2, 3, 5]
A_sub1 = A[idx1, :].sum(axis=1)
A_sub2 = A[idx2, :].sum(axis=1)
B = vstack((A_sub1, A_sub2))
But this gives me the summed up values just for the non-zero columns in the slice, so I can't combine it with the other slices because the number of columns in the summed slices are different.
I feel like there must be an easy way to do this. But I couldn't find any discussion of this online or in the documentation. What am I missing?
Thank you for your help
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