How to access sparse matrix elements?
To answer your title's question using a different technique than your question's details:
csc_matrix
gives you the method .nonzero()
.
Given:
>>> import numpy as np
>>> from scipy.sparse.csc import csc_matrix
>>>
>>> row = np.array( [0, 1, 3])
>>> col = np.array( [0, 2, 3])
>>> data = np.array([1, 4, 16])
>>> A = csc_matrix((data, (row, col)), shape=(4, 4))
You can access the indices poniting to non-zero data by:
>>> rows, cols = A.nonzero()
>>> rows
array([0, 1, 3], dtype=int32)
>>> cols
array([0, 2, 3], dtype=int32)
Which you can then use to access your data, without ever needing to make a dense version of your sparse matrix:
>>> [((i, j), A[i,j]) for i, j in zip(*A.nonzero())]
[((0, 0), 1), ((1, 2), 4), ((3, 3), 16)]
If it is for calculating TFIDF score using TfidfTransformer
, yu can get the IDF by tfidf.idf_
. Then the sparse array name, say 'a', a.toarray().
toarray
returns an ndarray; todense
returns a matrix. If you want a matrix, use todense
; otherwise, use toarray
.
A[1,:]
is itself a sparse matrix with shape (1, 60877). This is what you are printing, and it has only one row, so all the row coordinates are 0.
For example:
In [41]: a = csc_matrix([[1, 0, 0, 0], [0, 0, 10, 11], [0, 0, 0, 99]])
In [42]: a.todense()
Out[42]:
matrix([[ 1, 0, 0, 0],
[ 0, 0, 10, 11],
[ 0, 0, 0, 99]], dtype=int64)
In [43]: print(a[1, :])
(0, 2) 10
(0, 3) 11
In [44]: print(a)
(0, 0) 1
(1, 2) 10
(1, 3) 11
(2, 3) 99
In [45]: print(a[1, :].toarray())
[[ 0 0 10 11]]
You can select columns, but if there are no nonzero elements in the column, nothing is displayed when it is output with print
:
In [46]: a[:, 3].toarray()
Out[46]:
array([[ 0],
[11],
[99]])
In [47]: print(a[:,3])
(1, 0) 11
(2, 0) 99
In [48]: a[:, 1].toarray()
Out[48]:
array([[0],
[0],
[0]])
In [49]: print(a[:, 1])
In [50]:
The last print
call shows no output because the column a[:, 1]
has no nonzero elements.