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functools 源码阅读

functools主要包括这几个东西: wraps, partial, lru_cache, 还有一些内置 帮助函数例如 c3_mro。我们主要看上面三个:

wraps

wraps实现很简单,就是把a函数的某些属性拷贝到b函数。

partial

partial的实现,看下面代码就可以了:

class Partial:
    def __new__(*args, **kwargs):
        cls, func, *args = args

        self = super(Partial, cls).__new__(cls)
        self.func = func
        self.args = args
        self.kwargs = kwargs

        return self

    def __call__(*args, **kwargs):
        self, *new_args = args
        new_kwargs = self.kwargs.copy()
        new_kwargs.update(kwargs)
        return self.func(*self.args, *new_args, **new_kwargs)


mypartial = Partial  # noqa


if __name__ == "__main__":
    def foo(first, second, hello="world"):
        print("first, second, hello = %s, %s, %s" % (first, second, hello))

    bar = mypartial(foo, second=2, hello="hello")
    bar(1)

主要原理就是先用一个类来保存原函数的一些状态,然后重写 __call__ 方法,把 对应的已经partial的参数一起塞进去。

lru_cache(Least-recently-used cache)

这个比较有意思,def lru_cache(maxsize=128, typed=False):,如果maxsize为0, 那就直接返回函数结果,如果为None,那就直接用一个字典存储,如果为具体数值, 那么还会有一个环状双向链表来保存顺序。

lru_cache 的实现主要包括三个部分:

- `class _HashedSeq(list)`
- `def _make_key(...省略一把参数...)`
- `def lru_cache(maxsize=128, typed=False)`

其中第一个类用来保存hash值,第二个用来根据函数的参数生成key,第三个基于前两个 实现了lru_cache

下面是我的一个实现:

class Link:
    __slots__ = 'prev', 'next', 'key', 'value'


def _make_key(args, kwargs, kwargs_mark=(object(), )):
    key = args
    if kwargs:
        sorted_kwargs = sorted(kwargs.items())
        key += kwargs_mark
        for kwarg in sorted_kwargs:
            key += kwarg
    return hash(key)


def lru_cache(maxsize=128):
    def decorator(user_func):
        return _lru_cache_wrapper(user_func, maxsize)
    return decorator


def _lru_cache_wrapper(user_func, maxsize):
    cache = {}
    root = Link()
    root.prev, root.next, root.key, root.value = root, root, None, None

    def decorator(*args, **kwargs):
        nonlocal cache, root
        key = _make_key(args, kwargs)
        if key in cache:
            value = cache[key]
        else:
            value = user_func(*args, **kwargs)

        # 更新环状双向链表
        last = root.prev
        link = Link()
        link.prev = last
        link.next = root
        link.key, link.value = key, value
        root.prev = last.next = link

        if key not in cache:
            # 更新缓存信息和环状双向链表,因为缓存有大小限制
            cache[key] = value

            last = root.prev
            root = root.next
            root.prev = last
            last.next = root

        return value
    return decorator


if __name__ == "__main__":
    import time

    # 普通递归版fib函数
    start = time.time()

    def fib(x):
        if x == 0 or x == 1:
            return x
        else:
            return fib(x - 1) + fib(x - 2)

    result = fib(35)
    end = time.time()
    print("result: %s, use time: %.6f" % (result, end - start))

    # 为了方便看,还是不用函数形式的decorator,而是直接把fib函数抄一遍
    start = time.time()

    @lru_cache()
    def fib_cache(x):
        if x == 0 or x == 1:
            return x
        else:
            return fib_cache(x - 1) + fib_cache(x - 2)

    result = fib_cache(35)
    end = time.time()
    print("result: %s, use time: %.6f" % (result, end - start))

运行结果:

[email protected] tests: python myfunctools.py
result: 9227465, use time: 4.576693
result: 9227465, use time: 0.000146