Save / load scipy sparse csr_matrix in portable data format

edit: scipy 0.19 now has scipy.sparse.save_npz and scipy.sparse.load_npz.

from scipy import sparse

sparse.save_npz("yourmatrix.npz", your_matrix)
your_matrix_back = sparse.load_npz("yourmatrix.npz")

For both functions, the file argument may also be a file-like object (i.e. the result of open) instead of a filename.

Got an answer from the Scipy user group:

A csr_matrix has 3 data attributes that matter: .data, .indices, and .indptr. All are simple ndarrays, so numpy.save will work on them. Save the three arrays with numpy.save or numpy.savez, load them back with numpy.load, and then recreate the sparse matrix object with:

new_csr = csr_matrix((data, indices, indptr), shape=(M, N))

So for example:

def save_sparse_csr(filename, array):
    np.savez(filename, data=array.data, indices=array.indices,
             indptr=array.indptr, shape=array.shape)

def load_sparse_csr(filename):
    loader = np.load(filename)
    return csr_matrix((loader['data'], loader['indices'], loader['indptr']),
                      shape=loader['shape'])

Though you write, scipy.io.mmwrite and scipy.io.mmread don't work for you, I just want to add how they work. This question is the no. 1 Google hit, so I myself started with np.savez and pickle.dump before switching to the simple and obvious scipy-functions. They work for me and shouldn't be overseen by those who didn't tried them yet.

from scipy import sparse, io

m = sparse.csr_matrix([[0,0,0],[1,0,0],[0,1,0]])
m              # <3x3 sparse matrix of type '<type 'numpy.int64'>' with 2 stored elements in Compressed Sparse Row format>

io.mmwrite("test.mtx", m)
del m

newm = io.mmread("test.mtx")
newm           # <3x3 sparse matrix of type '<type 'numpy.int32'>' with 2 stored elements in COOrdinate format>
newm.tocsr()   # <3x3 sparse matrix of type '<type 'numpy.int32'>' with 2 stored elements in Compressed Sparse Row format>
newm.toarray() # array([[0, 0, 0], [1, 0, 0], [0, 1, 0]], dtype=int32)