python 的csr_python - 以便携式数据形式保存/加载scipy稀疏csr_matrix

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以下是使用Jupyter笔记本的三个最受欢迎的答案的性能比较。 输入是一个1M x 100K随机稀疏矩阵，密度为0.001，包含100M非零值：

from scipy.sparse import random

matrix = random(1000000, 100000, density=0.001, format='csr')

matrix

<1000000x100000 sparse matrix of type ''

with 100000000 stored elements in Compressed Sparse Row format>

cPickle/np.savez

from scipy.sparse import io

%time io.mmwrite('test_io.mtx', matrix)

CPU times: user 4min 37s, sys: 2.37 s, total: 4min 39s

Wall time: 4min 39s

%time matrix = io.mmread('test_io.mtx')

CPU times: user 2min 41s, sys: 1.63 s, total: 2min 43s

Wall time: 2min 43s

matrix

<1000000x100000 sparse matrix of type ''

with 100000000 stored elements in COOrdinate format>

Filesize: 3.0G.

(请注意，格式已从csr更改为coo)。

cPickle/np.savez

import numpy as np

from scipy.sparse import csr_matrix

def save_sparse_csr(filename, array):

# note that .npz extension is added automatically

np.savez(filename, data=array.data, indices=array.indices,

indptr=array.indptr, shape=array.shape)

def load_sparse_csr(filename):

# here we need to add .npz extension manually

loader = np.load(filename + '.npz')

return csr_matrix((loader['data'], loader['indices'], loader['indptr']),

shape=loader['shape'])

%time save_sparse_csr('test_savez', matrix)

CPU times: user 1.26 s, sys: 1.48 s, total: 2.74 s

Wall time: 2.74 s

%time matrix = load_sparse_csr('test_savez')

CPU times: user 1.18 s, sys: 548 ms, total: 1.73 s

Wall time: 1.73 s

matrix

<1000000x100000 sparse matrix of type ''

with 100000000 stored elements in Compressed Sparse Row format>

Filesize: 1.1G.

cPickle

import cPickle as pickle

def save_pickle(matrix, filename):

with open(filename, 'wb') as outfile:

pickle.dump(matrix, outfile, pickle.HIGHEST_PROTOCOL)

def load_pickle(filename):

with open(filename, 'rb') as infile:

matrix = pickle.load(infile)

return matrix

%time save_pickle(matrix, 'test_pickle.mtx')

CPU times: user 260 ms, sys: 888 ms, total: 1.15 s

Wall time: 1.15 s

%time matrix = load_pickle('test_pickle.mtx')

CPU times: user 376 ms, sys: 988 ms, total: 1.36 s

Wall time: 1.37 s

matrix

<1000000x100000 sparse matrix of type ''

with 100000000 stored elements in Compressed Sparse Row format>

Filesize: 1.1G.

注意：cPickle不适用于非常大的对象(请参阅此答案)。根据我的经验，它不适用于具有270M非零值的2.7M x 50k矩阵。cPickle解决方案效果很好。

结论

(基于这个简单的CSR矩阵测试)cPickle是最快的方法，但它不适用于非常大的矩阵，np.savez只是稍慢，而io.mmwrite慢得多，产生更大的文件并恢复到错误的格式。 所以np.savez是赢家。
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版权声明：本文为CSDN博主「weixin_39974223」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/weixin_39974223/article/details/111766769

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