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

如何以可移植格式保存/加载scipy稀疏csr_matrix?稀疏稀疏矩阵是在Python 3(Windows 64位)上创建的，以在Python 2(Linux 64位)上运行.最初，我使用pickle(协议= 2，fix_imports = True)，但是从Python 3.2.2(Windows 64位)到Python 2.7.2(Windows 32位)不起作用，并出现错误:

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numpy.save和numpy.load以及scipy.io.mmwrite()和scipy.io.mmread()，但这些方法均无效.

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

编辑:SciPy 1.19现在具有

edit: SciPy 1.19 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参数也可以是类似于文件的对象(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.

从Scipy用户组得到答案:

Got an answer from the Scipy user group:

csr_matrix具有3个重要的数据属性:.data，.indices和.indptr.所有的都是简单的ndarrays，因此numpy.save可以在它们上面工作.用numpy.save或numpy.savez保存这三个数组，用numpy.load装回它们，然后使用以下方法重新创建稀疏矩阵对象:

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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