You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
158 lines
5.4 KiB
Python
158 lines
5.4 KiB
Python
from __future__ import division, print_function, absolute_import
|
|
|
|
import sys
|
|
import numpy as np
|
|
import scipy.sparse
|
|
|
|
from scipy._lib._version import NumpyVersion
|
|
|
|
__all__ = ['save_npz', 'load_npz']
|
|
|
|
|
|
if NumpyVersion(np.__version__) >= '1.10.0':
|
|
# Make loading safe vs. malicious input
|
|
PICKLE_KWARGS = dict(allow_pickle=False)
|
|
else:
|
|
PICKLE_KWARGS = dict()
|
|
|
|
|
|
def save_npz(file, matrix, compressed=True):
|
|
""" Save a sparse matrix to a file using ``.npz`` format.
|
|
|
|
Parameters
|
|
----------
|
|
file : str or file-like object
|
|
Either the file name (string) or an open file (file-like object)
|
|
where the data will be saved. If file is a string, the ``.npz``
|
|
extension will be appended to the file name if it is not already
|
|
there.
|
|
matrix: spmatrix (format: ``csc``, ``csr``, ``bsr``, ``dia`` or coo``)
|
|
The sparse matrix to save.
|
|
compressed : bool, optional
|
|
Allow compressing the file. Default: True
|
|
|
|
See Also
|
|
--------
|
|
scipy.sparse.load_npz: Load a sparse matrix from a file using ``.npz`` format.
|
|
numpy.savez: Save several arrays into a ``.npz`` archive.
|
|
numpy.savez_compressed : Save several arrays into a compressed ``.npz`` archive.
|
|
|
|
Examples
|
|
--------
|
|
Store sparse matrix to disk, and load it again:
|
|
|
|
>>> import scipy.sparse
|
|
>>> sparse_matrix = scipy.sparse.csc_matrix(np.array([[0, 0, 3], [4, 0, 0]]))
|
|
>>> sparse_matrix
|
|
<2x3 sparse matrix of type '<class 'numpy.int64'>'
|
|
with 2 stored elements in Compressed Sparse Column format>
|
|
>>> sparse_matrix.todense()
|
|
matrix([[0, 0, 3],
|
|
[4, 0, 0]], dtype=int64)
|
|
|
|
>>> scipy.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix)
|
|
>>> sparse_matrix = scipy.sparse.load_npz('/tmp/sparse_matrix.npz')
|
|
|
|
>>> sparse_matrix
|
|
<2x3 sparse matrix of type '<class 'numpy.int64'>'
|
|
with 2 stored elements in Compressed Sparse Column format>
|
|
>>> sparse_matrix.todense()
|
|
matrix([[0, 0, 3],
|
|
[4, 0, 0]], dtype=int64)
|
|
"""
|
|
arrays_dict = {}
|
|
if matrix.format in ('csc', 'csr', 'bsr'):
|
|
arrays_dict.update(indices=matrix.indices, indptr=matrix.indptr)
|
|
elif matrix.format == 'dia':
|
|
arrays_dict.update(offsets=matrix.offsets)
|
|
elif matrix.format == 'coo':
|
|
arrays_dict.update(row=matrix.row, col=matrix.col)
|
|
else:
|
|
raise NotImplementedError('Save is not implemented for sparse matrix of format {}.'.format(matrix.format))
|
|
arrays_dict.update(
|
|
format=matrix.format.encode('ascii'),
|
|
shape=matrix.shape,
|
|
data=matrix.data
|
|
)
|
|
if compressed:
|
|
np.savez_compressed(file, **arrays_dict)
|
|
else:
|
|
np.savez(file, **arrays_dict)
|
|
|
|
|
|
def load_npz(file):
|
|
""" Load a sparse matrix from a file using ``.npz`` format.
|
|
|
|
Parameters
|
|
----------
|
|
file : str or file-like object
|
|
Either the file name (string) or an open file (file-like object)
|
|
where the data will be loaded.
|
|
|
|
Returns
|
|
-------
|
|
result : csc_matrix, csr_matrix, bsr_matrix, dia_matrix or coo_matrix
|
|
A sparse matrix containing the loaded data.
|
|
|
|
Raises
|
|
------
|
|
IOError
|
|
If the input file does not exist or cannot be read.
|
|
|
|
See Also
|
|
--------
|
|
scipy.sparse.save_npz: Save a sparse matrix to a file using ``.npz`` format.
|
|
numpy.load: Load several arrays from a ``.npz`` archive.
|
|
|
|
Examples
|
|
--------
|
|
Store sparse matrix to disk, and load it again:
|
|
|
|
>>> import scipy.sparse
|
|
>>> sparse_matrix = scipy.sparse.csc_matrix(np.array([[0, 0, 3], [4, 0, 0]]))
|
|
>>> sparse_matrix
|
|
<2x3 sparse matrix of type '<class 'numpy.int64'>'
|
|
with 2 stored elements in Compressed Sparse Column format>
|
|
>>> sparse_matrix.todense()
|
|
matrix([[0, 0, 3],
|
|
[4, 0, 0]], dtype=int64)
|
|
|
|
>>> scipy.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix)
|
|
>>> sparse_matrix = scipy.sparse.load_npz('/tmp/sparse_matrix.npz')
|
|
|
|
>>> sparse_matrix
|
|
<2x3 sparse matrix of type '<class 'numpy.int64'>'
|
|
with 2 stored elements in Compressed Sparse Column format>
|
|
>>> sparse_matrix.todense()
|
|
matrix([[0, 0, 3],
|
|
[4, 0, 0]], dtype=int64)
|
|
"""
|
|
|
|
with np.load(file, **PICKLE_KWARGS) as loaded:
|
|
try:
|
|
matrix_format = loaded['format']
|
|
except KeyError:
|
|
raise ValueError('The file {} does not contain a sparse matrix.'.format(file))
|
|
|
|
matrix_format = matrix_format.item()
|
|
|
|
if sys.version_info[0] >= 3 and not isinstance(matrix_format, str):
|
|
# Play safe with Python 2 vs 3 backward compatibility;
|
|
# files saved with Scipy < 1.0.0 may contain unicode or bytes.
|
|
matrix_format = matrix_format.decode('ascii')
|
|
|
|
try:
|
|
cls = getattr(scipy.sparse, '{}_matrix'.format(matrix_format))
|
|
except AttributeError:
|
|
raise ValueError('Unknown matrix format "{}"'.format(matrix_format))
|
|
|
|
if matrix_format in ('csc', 'csr', 'bsr'):
|
|
return cls((loaded['data'], loaded['indices'], loaded['indptr']), shape=loaded['shape'])
|
|
elif matrix_format == 'dia':
|
|
return cls((loaded['data'], loaded['offsets']), shape=loaded['shape'])
|
|
elif matrix_format == 'coo':
|
|
return cls((loaded['data'], (loaded['row'], loaded['col'])), shape=loaded['shape'])
|
|
else:
|
|
raise NotImplementedError('Load is not implemented for '
|
|
'sparse matrix of format {}.'.format(matrix_format))
|