以可移植数据格式保存/加载scipy稀疏csr_matrix

Question 1

如何csr_matrix以可移植格式保存/加载稀疏稀疏？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：

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

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

Question 2

编辑： SciPy 1.19现在具有scipy.sparse.save_npz和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（即的结果open），而不是文件名。

从Scipy用户组得到了答案：

一个csr_matrix有3个数据属性此事：.data，.indices，和.indptr。所有都是简单的ndarray，因此numpy.save可以在它们上使用。用numpy.save或保存三个数组，用numpy.savez加载它们numpy.load，然后用以下方法重新创建稀疏矩阵对象：
new_csr = csr_matrix((data, indices, indptr), shape=(M, N))

因此，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'])

Question 3

虽然你写的，scipy.io.mmwrite并且scipy.io.mmread不适合你的工作，我只想补充它们的工作原理。这个问题是没有。1 Google命中，所以我本人开始np.savez并开始使用pickle.dump简单明显的scipy函数。它们为我工作，不应由尚未尝试过它们的人监督。

from scipy import sparse, io

m = sparse.csr_matrix([[0,0,0],[1,0,0],[0,1,0]])
m              # <3x3 sparse matrix of type '<type 'numpy.int64'>' with 2 stored elements in Compressed Sparse Row format>

io.mmwrite("test.mtx", m)
del m

newm = io.mmread("test.mtx")
newm           # <3x3 sparse matrix of type '<type 'numpy.int32'>' with 2 stored elements in COOrdinate format>
newm.tocsr()   # <3x3 sparse matrix of type '<type 'numpy.int32'>' with 2 stored elements in Compressed Sparse Row format>
newm.toarray() # array([[0, 0, 0], [1, 0, 0], [0, 1, 0]], dtype=int32)

Question 4

这是使用Jupyter笔记本对三个最受欢迎的答案的性能比较。输入是一个密度为0.001的1M x 100K随机稀疏矩阵，其中包含100M非零值：

from scipy.sparse import random
matrix = random(1000000, 100000, density=0.001, format='csr')

matrix
<1000000x100000 sparse matrix of type '<type 'numpy.float64'>'
with 100000000 stored elements in Compressed Sparse Row format>

`io.mmwrite` / `io.mmread`

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 '<type 'numpy.float64'>'
with 100000000 stored elements in COOrdinate format>    

Filesize: 3.0G.

（请注意，格式已从csr更改为coo）。

`np.savez` / `np.load`

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 '<type 'numpy.float64'>'
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 '<type 'numpy.float64'>'
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）。以我的经验，它对于具有270M非零值的2.7M x 50k矩阵无效。 np.savez解决方案效果很好。

结论

（基于此针对CSR矩阵的简单测试） cPickle是最快的方法，但它不适用于非常大的矩阵，np.savez仅稍慢一些，而io.mmwrite慢得多，会产生更大的文件并恢复为错误的格式。所以np.savez在这里的赢家。

Question 5

现在您可以使用scipy.sparse.save_npz：https : //docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.save_npz.html

Question 6

假设您在两台机器上都有技巧，则只需使用即可pickle。

但是，腌制numpy数组时，请确保指定二进制协议。否则，您将得到一个巨大的文件。

无论如何，您应该能够做到这一点：

import cPickle as pickle
import numpy as np
import scipy.sparse

# Just for testing, let's make a dense array and convert it to a csr_matrix
x = np.random.random((10,10))
x = scipy.sparse.csr_matrix(x)

with open('test_sparse_array.dat', 'wb') as outfile:
    pickle.dump(x, outfile, pickle.HIGHEST_PROTOCOL)

然后可以使用以下命令加载它：

import cPickle as pickle

with open('test_sparse_array.dat', 'rb') as infile:
    x = pickle.load(infile)

Question 7

从scipy 0.19.0开始，您可以通过以下方式保存和加载稀疏矩阵：

from scipy import sparse

data = sparse.csr_matrix((3, 4))

#Save
sparse.save_npz('data_sparse.npz', data)

#Load
data = sparse.load_npz("data_sparse.npz")

Question 8

编辑显然，它很简单：

def sparse_matrix_tuples(m):
    yield from m.todok().items()

这将产生一个((i, j), value)元组，该元组易于序列化和反序列化。不知道如何将性能与下面的代码进行比较csr_matrix，但是肯定更简单。我将原始答案留在下面，希望它能为您提供更多信息。

加两分钱：对我来说，npz这不是可移植的，因为我不能用它轻松地将矩阵导出到非Python客户端（例如PostgreSQL，很高兴得到纠正）。因此，我希望获得稀疏矩阵的CSV输出（就像您将获得稀疏矩阵的CSV输出一样print()）。如何实现这一点取决于稀疏矩阵的表示。对于CSR矩阵，以下代码将输出CSV输出。您可以适应其他表示形式。

import numpy as np

def csr_matrix_tuples(m):
    # not using unique will lag on empty elements
    uindptr, uindptr_i = np.unique(m.indptr, return_index=True)
    for i, (start_index, end_index) in zip(uindptr_i, zip(uindptr[:-1], uindptr[1:])):
        for j, data in zip(m.indices[start_index:end_index], m.data[start_index:end_index]):
            yield (i, j, data)

for i, j, data in csr_matrix_tuples(my_csr_matrix):
    print(i, j, data, sep=',')

根据save_npz我的测试，它比当前实现慢了大约2倍。

Question 9

这就是我用来保存的内容lil_matrix。

import numpy as np
from scipy.sparse import lil_matrix

def save_sparse_lil(filename, array):
    # use np.savez_compressed(..) for compression
    np.savez(filename, dtype=array.dtype.str, data=array.data,
        rows=array.rows, shape=array.shape)

def load_sparse_lil(filename):
    loader = np.load(filename)
    result = lil_matrix(tuple(loader["shape"]), dtype=str(loader["dtype"]))
    result.data = loader["data"]
    result.rows = loader["rows"]
    return result

我必须说我发现NumPy的np.load（..）非常慢。这是我目前的解决方案，我觉得运行起来要快得多：

from scipy.sparse import lil_matrix
import numpy as np
import json

def lil_matrix_to_dict(myarray):
    result = {
        "dtype": myarray.dtype.str,
        "shape": myarray.shape,
        "data":  myarray.data,
        "rows":  myarray.rows
    }
    return result

def lil_matrix_from_dict(mydict):
    result = lil_matrix(tuple(mydict["shape"]), dtype=mydict["dtype"])
    result.data = np.array(mydict["data"])
    result.rows = np.array(mydict["rows"])
    return result

def load_lil_matrix(filename):
    result = None
    with open(filename, "r", encoding="utf-8") as infile:
        mydict = json.load(infile)
        result = lil_matrix_from_dict(mydict)
    return result

def save_lil_matrix(filename, myarray):
    with open(filename, "w", encoding="utf-8") as outfile:
        mydict = lil_matrix_to_dict(myarray)
        json.dump(mydict, outfile)

Question 10

这对我有用：

import numpy as np
import scipy.sparse as sp
x = sp.csr_matrix([1,2,3])
y = sp.csr_matrix([2,3,4])
np.savez(file, x=x, y=y)
npz = np.load(file)

>>> npz['x'].tolist()
<1x3 sparse matrix of type '<class 'numpy.int64'>'
    with 3 stored elements in Compressed Sparse Row format>

>>> npz['x'].tolist().toarray()
array([[1, 2, 3]], dtype=int64)

技巧是调用.tolist()将shape 0对象数组转换为原始对象。

Question 11

我被要求以简单通用的格式发送矩阵：

<x,y,value>

我结束了这个：

def save_sparse_matrix(m,filename):
    thefile = open(filename, 'w')
    nonZeros = np.array(m.nonzero())
    for entry in range(nonZeros.shape[1]):
        thefile.write("%s,%s,%s\n" % (nonZeros[0, entry], nonZeros[1, entry], m[nonZeros[0, entry], nonZeros[1, entry]]))

以可移植数据格式保存/加载scipy稀疏csr_matrix

io.mmwrite / io.mmread

np.savez / np.load

cPickle

结论

`io.mmwrite` / `io.mmread`

`np.savez` / `np.load`

`cPickle`