Storing scipy sparse matrix as HDF5

Question

I want to compress and store a humongous Scipy matrix in HDF5 format. How do I do this? I\'ve tried the below code:

a = csr_matrix((dat, (row, col)), shape=(9479         


        

Answer 1

A csr matrix stores it's values in 3 arrays. It is not an array or array subclass, so h5py cannot save it directly. The best you can do is save the attributes, and recreate the matrix on loading:

In [248]: M = sparse.random(5,10,.1, 'csr')
In [249]: M
Out[249]: 
<5x10 sparse matrix of type ''
    with 5 stored elements in Compressed Sparse Row format>
In [250]: M.data
Out[250]: array([ 0.91615298,  0.49907752,  0.09197862,  0.90442401,  0.93772772])
In [251]: M.indptr
Out[251]: array([0, 0, 1, 2, 3, 5], dtype=int32)
In [252]: M.indices
Out[252]: array([5, 7, 5, 2, 6], dtype=int32)
In [253]: M.data
Out[253]: array([ 0.91615298,  0.49907752,  0.09197862,  0.90442401,  0.93772772])

coo format has data, row, col attributes, basically the same as the (dat, (row, col)) you use to create your a.

In [254]: M.tocoo().row
Out[254]: array([1, 2, 3, 4, 4], dtype=int32)

The new save_npz function does:

arrays_dict = dict(format=matrix.format, shape=matrix.shape, data=matrix.data)
if matrix.format in ('csc', 'csr', 'bsr'):
    arrays_dict.update(indices=matrix.indices, indptr=matrix.indptr)
...
elif matrix.format == 'coo':
    arrays_dict.update(row=matrix.row, col=matrix.col)
...
np.savez(file, **arrays_dict)

In other words it collects the relevant attributes in a dictionary and uses savez to create the zip archive.

The same sort of method could be used with a h5py file. More on that save_npz in a recent SO question, with links to the source code.

save_npz method missing from scipy.sparse

See if you can get this working. If you can create a csr matrix, you can recreate it from its attributes (or the coo equivalents). I can make a working example if needed.

csr to h5py example

import numpy as np
import h5py
from scipy import sparse

M = sparse.random(10,10,.2, 'csr')
print(repr(M))

print(M.data)
print(M.indices)
print(M.indptr)

f = h5py.File('sparse.h5','w')
g = f.create_group('Mcsr')
g.create_dataset('data',data=M.data)
g.create_dataset('indptr',data=M.indptr)
g.create_dataset('indices',data=M.indices)
g.attrs['shape'] = M.shape
f.close()

f = h5py.File('sparse.h5','r')
print(list(f.keys()))
print(list(f['Mcsr'].keys()))

g2 = f['Mcsr']
print(g2.attrs['shape'])

M1 = sparse.csr_matrix((g2['data'][:],g2['indices'][:],
    g2['indptr'][:]), g2.attrs['shape'])
print(repr(M1))
print(np.allclose(M1.A, M.A))
f.close()

producing

1314:~/mypy$ python3 stack43390038.py 
<10x10 sparse matrix of type ''
    with 20 stored elements in Compressed Sparse Row format>
[ 0.13640389  0.92698959 ....  0.7762265 ]
[4 5 0 3 0 2 0 2 5 6 7 1 7 9 1 3 4 6 8 9]
[ 0  2  4  6  9 11 11 11 14 19 20]
['Mcsr']
['data', 'indices', 'indptr']
[10 10]
<10x10 sparse matrix of type ''
    with 20 stored elements in Compressed Sparse Row format>
True

coo alternative

Mo = M.tocoo()
g = f.create_group('Mcoo')
g.create_dataset('data', data=Mo.data)
g.create_dataset('row', data=Mo.row)
g.create_dataset('col', data=Mo.col)
g.attrs['shape'] = Mo.shape

g2 = f['Mcoo']
M2 = sparse.coo_matrix((g2['data'], (g2['row'], g2['col'])),
   g2.attrs['shape'])   # don't need the [:]
# could also use sparse.csr_matrix or M2.tocsr()