以可移植数据格式保存/加载 scipy 稀疏 csr_matrix [英] Save / load scipy sparse csr_matrix in portable data format

问题描述

如何以可移植的格式保存/加载 scipy 稀疏 csr_matrix?scipy 稀疏矩阵是在 Python 3(Windows 64 位)上创建的，以在 Python 2(Linux 64 位)上运行.最初，我使用了 pickle(协议=2 和 fix_imports=True)，但是从 Python 3.2.2(Windows 64 位)到 Python 2.7.2(Windows 32 位)这不起作用并得到错误:

How do you save/load a scipy sparse csr_matrix in a portable format? The scipy sparse matrix is created on Python 3 (Windows 64-bit) to run on Python 2 (Linux 64-bit). Initially, I used pickle (with protocol=2 and fix_imports=True) but this didn't work going from Python 3.2.2 (Windows 64-bit) to Python 2.7.2 (Windows 32-bit) and got the error:

TypeError: ('data type not understood', <built-in function _reconstruct>, (<type 'numpy.ndarray'>, (0,), '[98]')).

接下来，尝试了 numpy.save 和 numpy.load 以及 scipy.io.mmwrite() 和 scipy.io.mmread() 并且这些方法都不起作用.

Next, tried numpy.save and numpy.load as well as scipy.io.mmwrite() and scipy.io.mmread() and none of these methods worked either.

推荐答案

scipy 0.19 现在有 scipy.sparse.save_npz 和 scipy.sparse.load_npz.

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")

对于这两个函数，file 参数也可以是类似文件的对象(即 open 的结果)而不是文件名.

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

得到了 Scipy 用户组的答复:

Got an answer from the Scipy user group:

csr_matrix 有 3 个重要的数据属性:.data、.indices 和 .indptr.所有都是简单的 ndarrays，所以 numpy.save 可以处理它们.用 numpy.save 或 numpy.savez 保存三个数组，用 numpy.load 加载它们，然后用:

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))

例如:

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'])

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