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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))
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