提升代码执行速度：使用lru_cache缓存函数避免重复计算

函数执行速度慢时，可以使用 lru_cache 缓存函数结果，避免重复计算，可极大地加快程序执行的速度。

1. 示例

以下函数用于统计元音字母的个数：

from tools import measure  # 自定义计时器函数

def count_vowels(sentence: str) -> int:
    return sum(sentence.count(vowel) for vowel in 'aeiouAEIOU')

@measure
def main():
    sentences = [
        'hello world',
        'this is a test sentence',
        'python is awesome',
    ]
    for sentence in sentences:
        for i in range(1_000_000):
            count_vowels(sentence)


if __name__ == '__main__':
    main()

接下来，我们使用 lru_cache 缓存函数 count_vowels，这极大加快执行速度。

from functools import lru_cache
from tools import measure  # (1)!

@lru_cache(maxsize=128, typed=False)
def count_vowels(sentence: str) -> int:
    return sum(sentence.count(vowel) for vowel in 'aeiouAEIOU')


@measure
def main() -> None:
    sentences: list[str] = [
        'hello world',
        'this is a test sentence',
        'python is awesome',
    ]
    for sentence in sentences:
        for i in range(1_000_000):
            count_vowels(sentence)


if __name__ == '__main__':
    main()
    print(count_vowels.cache_info())

1. tools.measure 是一个装饰器，用于测量函数执行时间。

2. 斐波那契缓存调用

使用 lru_cache 斐波那契数列的递归实现，你会发现连续调用时函数非常执行快到难以置信：

from functools import lru_cache

@lru_cache(maxsize=None)
def fib(n: int) -> int:
    if n < 2:
        return n
    return fib(n - 1) + fib(n - 2)

if __name__ == '__main__':
    fib(35)
    print(count_vowels.cache_info())