保留磁盘上的numpy数组的最佳方法

Question

我正在寻找一种快速保存大型numpy数组的方法.我想以二进制格式将它们保存到磁盘,然后相对快速地将它们读回内存.不幸的是,cPickle还不够快.

我找到了numpy.savez和numpy.load.但奇怪的是,numpy.load将npy文件加载到"memory-map"中.这意味着定期操作数组确实很慢.例如,像这样的东西会非常慢:

#!/usr/bin/python
import numpy as np;
import time; 
from tempfile import TemporaryFile

n = 10000000;

a = np.arange(n)
b = np.arange(n) * 10
c = np.arange(n) * -0.5

file = TemporaryFile()
np.savez(file,a = a, b = b, c = c);

file.seek(0)
t = time.time()
z = np.load(file)
print "loading time = ", time.time() - t

t = time.time()
aa = z['a']
bb = z['b']
cc = z['c']
print "assigning time = ", time.time() - t;

更确切地说,第一行将非常快,但分配数组的其余行obj非常慢:

loading time =  0.000220775604248
assining time =  2.72940087318

有没有更好的方法来保留numpy数组？理想情况下,我希望能够在一个文件中存储多个数组.

Answer 1

我已经比较了许多存储numpy数组的方法的性能(空间和时间).他们中很少有人支持每个文件多个数组,但无论如何它可能都很有用.

对于密集数据,Npy和二进制文件都非常快且很小.如果数据是稀疏的或非常结构化的,您可能希望将npz与压缩一起使用,这将节省大量空间,但会花费一些加载时间.

如果可移植性是个问题,二进制文件优于npy.如果人类的可读性很重要,那么你将不得不牺牲很多性能,但使用csv(当然也非常便携)可以很好地实现.

github repo提供了更多详细信息和代码.

Answer 2

我是hdf5的忠实粉丝,用于存储大型numpy数组.在python中处理hdf5有两种选择:

http://www.pytables.org/

http://www.h5py.org/

两者都旨在有效地使用numpy数组.

Answer 3

现在有一个基于HDF5的克隆pickle叫hickle!

https://github.com/telegraphic/hickle

import hickle as hkl 

data = { 'name' : 'test', 'data_arr' : [1, 2, 3, 4] }

# Dump data to file
hkl.dump( data, 'new_data_file.hkl' )

# Load data from file
data2 = hkl.load( 'new_data_file.hkl' )

print( data == data2 )

编辑:

通过执行以下操作,还可以直接"腌制"到压缩存档中:

import pickle, gzip, lzma, bz2

pickle.dump( data, gzip.open( 'data.pkl.gz',   'wb' ) )
pickle.dump( data, lzma.open( 'data.pkl.lzma', 'wb' ) )
pickle.dump( data,  bz2.open( 'data.pkl.bz2',  'wb' ) )

附录

import numpy as np
import matplotlib.pyplot as plt
import pickle, os, time
import gzip, lzma, bz2, h5py

compressions = [ 'pickle', 'h5py', 'gzip', 'lzma', 'bz2' ]
labels = [ 'pickle', 'h5py', 'pickle+gzip', 'pickle+lzma', 'pickle+bz2' ]
size = 1000

data = {}

# Random data
data['random'] = np.random.random((size, size))

# Not that random data
data['semi-random'] = np.zeros((size, size))
for i in range(size):
    for j in range(size):
        data['semi-random'][i,j] = np.sum(data['random'][i,:]) + np.sum(data['random'][:,j])

# Not random data
data['not-random'] = np.arange( size*size, dtype=np.float64 ).reshape( (size, size) )

sizes = {}

for key in data:

    sizes[key] = {}

    for compression in compressions:

        if compression == 'pickle':
            time_start = time.time()
            pickle.dump( data[key], open( 'data.pkl', 'wb' ) )
            time_tot = time.time() - time_start
            sizes[key]['pickle'] = ( os.path.getsize( 'data.pkl' ) * 10**(-6), time_tot )
            os.remove( 'data.pkl' )

        elif compression == 'h5py':
            time_start = time.time()
            with h5py.File( 'data.pkl.{}'.format(compression), 'w' ) as h5f:
                h5f.create_dataset('data', data=data[key])
            time_tot = time.time() - time_start
            sizes[key][compression] = ( os.path.getsize( 'data.pkl.{}'.format(compression) ) * 10**(-6), time_tot)
            os.remove( 'data.pkl.{}'.format(compression) )

        else:
            time_start = time.time()
            pickle.dump( data[key], eval(compression).open( 'data.pkl.{}'.format(compression), 'wb' ) )
            time_tot = time.time() - time_start
            sizes[key][ labels[ compressions.index(compression) ] ] = ( os.path.getsize( 'data.pkl.{}'.format(compression) ) * 10**(-6), time_tot )
            os.remove( 'data.pkl.{}'.format(compression) )


f, ax_size = plt.subplots()
ax_time = ax_size.twinx()

x_ticks = labels
x = np.arange( len(x_ticks) )

y_size = {}
y_time = {}
for key in data:
    y_size[key] = [ sizes[key][ x_ticks[i] ][0] for i in x ]
    y_time[key] = [ sizes[key][ x_ticks[i] ][1] for i in x ]

width = .2
viridis = plt.cm.viridis

p1 = ax_size.bar( x-width, y_size['random']       , width, color = viridis(0)  )
p2 = ax_size.bar( x      , y_size['semi-random']  , width, color = viridis(.45))
p3 = ax_size.bar( x+width, y_size['not-random']   , width, color = viridis(.9) )

p4 = ax_time.bar( x-width, y_time['random']  , .02, color = 'red')
ax_time.bar( x      , y_time['semi-random']  , .02, color = 'red')
ax_time.bar( x+width, y_time['not-random']   , .02, color = 'red')

ax_size.legend( (p1, p2, p3, p4), ('random', 'semi-random', 'not-random', 'saving time'), loc='upper center',bbox_to_anchor=(.5, -.1), ncol=4 )
ax_size.set_xticks( x )
ax_size.set_xticklabels( x_ticks )

f.suptitle( 'Pickle Compression Comparison' )
ax_size.set_ylabel( 'Size [MB]' )
ax_time.set_ylabel( 'Time [s]' )

f.savefig( 'sizes.pdf', bbox_inches='tight' )

Answer 4

savez()将数据保存在zip文件中,压缩和解压缩文件可能需要一些时间.您可以使用save()和load()函数:

f = file("tmp.bin","wb")
np.save(f,a)
np.save(f,b)
np.save(f,c)
f.close()

f = file("tmp.bin","rb")
aa = np.load(f)
bb = np.load(f)
cc = np.load(f)
f.close()

要将多个数组保存在一个文件中,只需先打开文件,然后按顺序保存或加载数组.

Answer 5

另一种有效存储numpy数组的可能性是Bloscpack:

#!/usr/bin/python
import numpy as np
import bloscpack as bp
import time

n = 10000000

a = np.arange(n)
b = np.arange(n) * 10
c = np.arange(n) * -0.5
tsizeMB = sum(i.size*i.itemsize for i in (a,b,c)) / 2**20.

blosc_args = bp.DEFAULT_BLOSC_ARGS
blosc_args['clevel'] = 6
t = time.time()
bp.pack_ndarray_file(a, 'a.blp', blosc_args=blosc_args)
bp.pack_ndarray_file(b, 'b.blp', blosc_args=blosc_args)
bp.pack_ndarray_file(c, 'c.blp', blosc_args=blosc_args)
t1 = time.time() - t
print "store time = %.2f (%.2f MB/s)" % (t1, tsizeMB / t1)

t = time.time()
a1 = bp.unpack_ndarray_file('a.blp')
b1 = bp.unpack_ndarray_file('b.blp')
c1 = bp.unpack_ndarray_file('c.blp')
t1 = time.time() - t
print "loading time = %.2f (%.2f MB/s)" % (t1, tsizeMB / t1)

和我的笔记本电脑的输出(一个相对较旧的MacBook Air与Core2处理器):

$ python store-blpk.py
store time = 0.19 (1216.45 MB/s)
loading time = 0.25 (898.08 MB/s)

这意味着它可以非常快速地存储,即瓶颈通常是磁盘.但是,由于压缩比非常好,因此有效速度乘以压缩比.以下是这些76 MB阵列的大小:

$ ll -h *.blp
-rw-r--r--  1 faltet  staff   921K Mar  6 13:50 a.blp
-rw-r--r--  1 faltet  staff   2.2M Mar  6 13:50 b.blp
-rw-r--r--  1 faltet  staff   1.4M Mar  6 13:50 c.blp

请注意,使用Blosc压缩机是实现这一目标的基础.相同的脚本,但使用'clevel'= 0(即禁用压缩):

$ python bench/store-blpk.py
store time = 3.36 (68.04 MB/s)
loading time = 2.61 (87.80 MB/s)

显然是磁盘性能的瓶颈.

Answer 6

查找时间很慢,因为在mmap调用load方法时使用时不会将数组内容加载到内存中.当需要特定数据时,数据是延迟加载的.在您的情况下,这发生在查找中.但第二次查找不会那么慢.

这是一个很好的功能,mmap当你有一个大数组时,你不必将整个数据加载到内存中.

要解决你可以使用joblib,你可以使用joblib.dump甚至两个或更多来转储你想要的任何对象numpy arrays,请参阅示例

firstArray = np.arange(100)
secondArray = np.arange(50)
# I will put two arrays in dictionary and save to one file
my_dict = {'first' : firstArray, 'second' : secondArray}
joblib.dump(my_dict, 'file_name.dat')