Separation of a sparse matrix into two

Question

Question: How can I split 1 sparse matrix into 2, based on the values ​​in the list?

That is, I have a sparse matrix X :

 >>print type(X) <class 'scipy.sparse.csr.csr_matrix'> 

which I imagine in my head as a list of lists to look like this:

 >>print X.todense() [[1,3,4] [3,2,2] [4,8,1]] 

And I have a list y that looks like this:

 y = [-1, 3, -4] 

How can I separate X from two sparse matrices, depending on whether the corresponding value in y positive or negative? For example, how can I get:

 >>print X_pos.todense() [[3,2,2]] >>print X_neg.todense() [[1,3,4] [4,8,1]] 

The result ( X_pos and X_neg ) should also be sparse matrices, obviously, since it just splits the sparse matrix to begin with.

Thanks!

Answer 1

Use np.where to create two arrays of indexes for positive and negative y values, and then use them to index into your sparse matrix.

 >>> X = csr_matrix([[1,3,4], [3,2,2], [4,8,1]]) >>> y = np.array([-1, 3, -4]) >>> y_pos = np.where(y > 0)[0] >>> y_neg = np.where(y < 0)[0] >>> X_pos = X[y_pos] >>> X_neg = X[y_neg] 

You should now have CSR matrices containing the required elements:

 >>> X_pos <1x3 sparse matrix of type '<type 'numpy.int64'>' with 3 stored elements in Compressed Sparse Row format> >>> X_neg <2x3 sparse matrix of type '<type 'numpy.int64'>' with 6 stored elements in Compressed Sparse Row format> >>> X_pos.A array([[3, 2, 2]]) >>> X_neg.A array([[1, 3, 4], [4, 8, 1]])