在python中快速/优化N-gram实现 [英] Fast/Optimize N-gram implementations in python

问题描述

哪个ngram实现在python中最快?

Which ngram implementation is fastest in python?

我尝试剖析nltk和scott的zip( http ://locallyoptimal.com/blog/2013/01/20/elegant-n-gram-generation-in-python/):

I've tried to profile nltk's vs scott's zip (http://locallyoptimal.com/blog/2013/01/20/elegant-n-gram-generation-in-python/):

from nltk.util import ngrams as nltkngram
import this, time

def zipngram(text,n=2):
  return zip(*[text.split()[i:] for i in range(n)])

text = this.s

start = time.time()
nltkngram(text.split(), n=2)
print time.time() - start

start = time.time()
zipngram(text, n=2)
print time.time() - start

[出]

0.000213146209717
6.50882720947e-05

在python中有更快的实现生成ngram的实现吗?

Is there any faster implementation for generating ngrams in python?

推荐答案

一些尝试进行了一些分析.我以为使用发电机可以提高速度.但是与原始版本的细微修​​改相比，改进并不明显.但是，如果您不需要同时使用完整列表，则生成器功能应该更快.

Some attempts with some profiling. I thought using generators could improve the speed here. But the improvement was not noticeable compared to a slight modification of the original. But if you don't need the full list at the same time, the generator functions should be faster.

import timeit
from itertools import tee, izip, islice

def isplit(source, sep):
    sepsize = len(sep)
    start = 0
    while True:
        idx = source.find(sep, start)
        if idx == -1:
            yield source[start:]
            return
        yield source[start:idx]
        start = idx + sepsize

def pairwise(iterable, n=2):
    return izip(*(islice(it, pos, None) for pos, it in enumerate(tee(iterable, n))))

def zipngram(text, n=2):
    return zip(*[text.split()[i:] for i in range(n)])

def zipngram2(text, n=2):
    words = text.split()
    return pairwise(words, n)


def zipngram3(text, n=2):
    words = text.split()
    return zip(*[words[i:] for i in range(n)])

def zipngram4(text, n=2):
    words = isplit(text, ' ')
    return pairwise(words, n)


s = "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum."
s = s * 10 ** 3

res = []
for n in range(15):

    a = timeit.timeit('zipngram(s, n)', 'from __main__ import zipngram, s, n', number=100)
    b = timeit.timeit('list(zipngram2(s, n))', 'from __main__ import zipngram2, s, n', number=100)
    c = timeit.timeit('zipngram3(s, n)', 'from __main__ import zipngram3, s, n', number=100)
    d = timeit.timeit('list(zipngram4(s, n))', 'from __main__ import zipngram4, s, n', number=100)

    res.append((a, b, c, d))

a, b, c, d = zip(*res)

import matplotlib.pyplot as plt

plt.plot(a, label="zipngram")
plt.plot(b, label="zipngram2")
plt.plot(c, label="zipngram3")
plt.plot(d, label="zipngram4")
plt.legend(loc=0)
plt.show()

对于此测试数据，zipngram2和zipngram3似乎是最快的.

For this test data, zipngram2 and zipngram3 seems to be the fastest by a good margin.

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