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))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(npzfilename):
loader = np.load(npzfilename)
return csr_matrix(( loader['data'], loader['indices'], loader['indptr']),shape = loader['shape'])
def save_ids_array(filename,array):
np.savez(filename+'.ids',data = array)
def load_ids_array(npzfilename):
loader = np.load(npzfilename)
return loader['data']