10

我有一个程序，每行读取 3 个字符串 50000。然后它会做其他事情。读取文件并转换为整数的部分占用了总运行时间的 80%。

我的代码片段如下：

import time
file = open ('E:/temp/edges_big.txt').readlines()
start_time = time.time()
for line in file[1:]:
    label1, label2, edge = line.strip().split()
    # label1 = int(label1); label2 = int(label2); edge = float(edge)
    # Rest of the loop deleted
print ('processing file took ', time.time() - start_time, "seconds")

以上大约需要0.84 秒。现在，当我取消注释该行时

label1 = int(label1);label2 = int(label2);edge = float(edge)

运行时间上升到大约3.42 秒。

输入文件的格式为：str1 str2 str3每行

功能int()很float()慢吗？我该如何优化呢？

4

3 回答 3

4

如果文件在操作系统缓存中，那么在我的机器上解析文件需要几毫秒：

name                                 time ratio comment
read_read                        145 usec  1.00 big.txt
read_readtxt                    2.07 msec 14.29 big.txt
read_readlines                  4.94 msec 34.11 big.txt
read_james_otigo                29.3 msec 201.88 big.txt
read_james_otigo_with_int_float 82.9 msec 571.70 big.txt
read_map_local                  93.1 msec 642.23 big.txt
read_map                        95.6 msec 659.57 big.txt
read_numpy_loadtxt               321 msec 2213.66 big.txt

read_*()函数在哪里：

def read_read(filename):
    with open(filename, 'rb') as file:
        data = file.read()

def read_readtxt(filename):
    with open(filename, 'rU') as file:
        text = file.read()

def read_readlines(filename):
    with open(filename, 'rU') as file:
        lines = file.readlines()

def read_james_otigo(filename):
    file = open (filename).readlines()
    for line in file[1:]:
        label1, label2, edge = line.strip().split()

def read_james_otigo_with_int_float(filename):
    file = open (filename).readlines()
    for line in file[1:]:
        label1, label2, edge = line.strip().split()
        label1 = int(label1); label2 = int(label2); edge = float(edge)

def read_map(filename):
    with open(filename) as file:
        L = [(int(l1), int(l2), float(edge))
             for line in file
             for l1, l2, edge in [line.split()] if line.strip()]

def read_map_local(filename, _i=int, _f=float):
    with open(filename) as file:
        L = [(_i(l1), _i(l2), _f(edge))
             for line in file
             for l1, l2, edge in [line.split()] if line.strip()]

import numpy as np

def read_numpy_loadtxt(filename):
    a = np.loadtxt('big.txt', dtype=[('label1', 'i'),
                                     ('label2', 'i'),
                                     ('edge', 'f')])

并big.txt使用以下方法生成：

#!/usr/bin/env python
import numpy as np

n = 50000
a = np.random.random_integers(low=0, high=1<<10, size=2*n).reshape(-1, 2)
np.savetxt('big.txt', np.c_[a, np.random.rand(n)], fmt='%i %i %s')

它产生 50000 行：

150 952 0.355243621018
582 98 0.227592557278
478 409 0.546382780254
46 879 0.177980983303
...

要重现结果，请下载代码并运行：

# write big.txt
python generate-file.py
# run benchmark
python read-array.py
于 2012-12-13T14:19:25.677 回答
3

我能得到几乎和你一样的时间。我认为问题出在我的代码上：

read_james_otigo                  40 msec big.txt
read_james_otigo_with_int_float  116 msec big.txt
read_map                         134 msec big.txt
read_map_local                   131 msec big.txt
read_numpy_loadtxt               400 msec big.txt
read_read                        488 usec big.txt
read_readlines                  9.24 msec big.txt
read_readtxt                    4.36 msec big.txt

name                                 time ratio comment
read_read                        488 usec  1.00 big.txt
read_readtxt                    4.36 msec  8.95 big.txt
read_readlines                  9.24 msec 18.95 big.txt
read_james_otigo                  40 msec 82.13 big.txt
read_james_otigo_with_int_float  116 msec 238.64 big.txt
read_map_local                   131 msec 268.05 big.txt
read_map                         134 msec 274.87 big.txt
read_numpy_loadtxt               400 msec 819.42 big.txt


read_james_otigo                39.4 msec big.txt
read_readtxt                    4.37 msec big.txt
read_readlines                  9.21 msec big.txt
read_map_local                   131 msec big.txt
read_james_otigo_with_int_float  116 msec big.txt
read_map                         134 msec big.txt
read_read                        487 usec big.txt
read_numpy_loadtxt               398 msec big.txt

name                                 time ratio comment
read_read                        487 usec  1.00 big.txt
read_readtxt                    4.37 msec  8.96 big.txt
read_readlines                  9.21 msec 18.90 big.txt
read_james_otigo                39.4 msec 80.81 big.txt
read_james_otigo_with_int_float  116 msec 238.51 big.txt
read_map_local                   131 msec 268.84 big.txt
read_map                         134 msec 275.11 big.txt
read_numpy_loadtxt               398 msec 816.71 big.txt
于 2012-12-13T16:30:39.480 回答
1

我根本无法重现这一点。

我生成了一个 50000 行的文件，每行包含三个随机数（两个整数，一个浮点数），用空格分隔。

然后我在那个文件上运行了你的脚本。在我三岁的电脑上，原始脚本在 0.05 秒内完成，未注释行的脚本需要 0.15 秒才能完成。当然，字符串到 int/float 的转换需要更长的时间，但肯定不是几秒钟的时间。除非您的目标机器是运行嵌入式 Windows CE 的烤面包机。

于 2012-12-13T13:09:03.537 回答