Python中更快的缩编技术

我试图找到一种使用NLTK Word Net Lemmatizer对列表(命名文本)中的单词进行词法化的更快方法.显然,这是我整个程序中最耗时的步骤(使用cProfiler查找相同的步骤).

以下是我正在尝试优化速度的一段代码-

def lemmed(text):
    l = len(text)
    i = 0
    wnl = WordNetLemmatizer()
    while (i<l):
        text[i] = wnl.lemmatize(text[i])
        i = i + 1
    return text

使用lemmatizer会使我的性能降低20倍.任何帮助,将不胜感激.

解决方法:

如果您有几个核心可用,请尝试使用多处理库：

from nltk import WordNetLemmatizer
from multiprocessing import Pool

def lemmed(text, cores=6): # tweak cores as needed
    with Pool(processes=cores) as pool:
        wnl = WordNetLemmatizer()
        result = pool.map(wnl.lemmatize, text)
    return result


sample_text = ['tests', 'friends', 'hello'] * (10 ** 6)

lemmed_text = lemmed(sample_text)

assert len(sample_text) == len(lemmed_text) == (10 ** 6) * 3

print(lemmed_text[:3])
# => ['test', 'friend', 'hello']

标签：performance,python-3-x,nltk,lemmatization,python
来源： https://codeday.me/bug/20191118/2028541.html