Question

我想要的是一个memoization装饰器：

可以使用参数和关键字参数来记忆实例方法
有一个可以通过一次调用清除（全局）的缓存（相对于使用每个函数缓存的这个缓存：python resettable instance method memoization decorator）
效率相当

我已经调整了一个我看到的例子并想出了以下内容：

import functools

class Memoized(object):
  """Decorator that caches a function's return value each time it is called.
  If called later with the same arguments, the cached value is returned, and
  not re-evaluated.
  """

  __cache = {}

  def __init__(self, func):
    self.func = func
    self.key = (func.__module__, func.__name__)

  def __call__(self, *args):
    try:
      return Memoized.__cache[self.key][args]
    except KeyError:
      value = self.func(*args)
      Memoized.__cache[self.key] = {args : value}
      return value
    except TypeError:
      # uncachable -- for instance, passing a list as an argument.
      # Better to not cache than to blow up entirely.
      return self.func(*args)

  def __get__(self, obj, objtype):
    """Support instance methods."""
    return functools.partial(self.__call__, obj)

  @staticmethod
  def reset():
    Memoized.__cache = {}

我的问题是缓存部分似乎涉及很多开销（例如，用于递归函数）。使用以下函数作为示例，我可以使用非memoized版本调用fib（30）十次，而不是记忆版本。

def fib(n):

   if n in (0, 1):
      return n
   return fib(n-1) + fib(n-2)

有人能建议更好的方法来编写这个装饰器吗？（或指向一个更好（即更快）的装饰器，做我想要的）。我对保留方法签名或帮助内省工具“了解”有关装饰函数的任何内容不感兴趣。

感谢。

P.S。使用python 2.7

Answer 1

您实际上并未缓存任何数据，因为每次设置新的缓存值都会覆盖前一个数据：

Memoized.__cache[self.key] = {args : value}

例如

import functools

class Memoized(object):
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.
    """

    cache = {}

    def __init__(self, func):
        self.func = func
        self.key = (func.__module__, func.__name__)
        self.cache[self.key] = {}

    def __call__(self, *args):
      try:
          return Memoized.cache[self.key][args]
      except KeyError:
          value = self.func(*args)
          Memoized.cache[self.key][args] = value
          return value
      except TypeError:
          # uncachable -- for instance, passing a list as an argument.
          # Better to not cache than to blow up entirely.
          return self.func(*args)

    def __get__(self, obj, objtype):
        """Support instance methods."""
        return functools.partial(self.__call__, obj)

    @staticmethod
    def reset():
        Memoized.cache = {}

fib（30）没有缓存：2.86742401123
fib（30）with caching：0.000198125839233

其他一些说明：

不要使用__prefix;没有理由在这里，它只是丑化了代码。
不使用单个整体的类属性dict，而是为每个Memoized实例提供自己的dict，并保留Memoized个对象的注册表。这改善了封装，并消除了依赖于模块和函数名称的奇怪现象。

import functools
import weakref

class Memoized(object):
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.

    >>> counter = 0
    >>> @Memoized
    ... def count():
    ...     global counter
    ...     counter += 1
    ...     return counter

    >>> counter = 0
    >>> Memoized.reset()
    >>> count()
    1
    >>> count()
    1
    >>> Memoized.reset()
    >>> count()
    2

    >>> class test(object):
    ...     @Memoized
    ...     def func(self):
    ...         global counter
    ...         counter += 1
    ...         return counter
    >>> testobject = test()
    >>> counter = 0
    >>> testobject.func()
    1
    >>> testobject.func()
    1
    >>> Memoized.reset()
    >>> testobject.func()
    2
    """

    caches = weakref.WeakSet()

    def __init__(self, func):
        self.func = func
        self.cache = {}
        Memoized.caches.add(self)

    def __call__(self, *args):
      try:
          return self.cache[args]
      except KeyError:
          value = self.func(*args)
          self.cache[args] = value
          return value
      except TypeError:
          # uncachable -- for instance, passing a list as an argument.
          # Better to not cache than to blow up entirely.
          return self.func(*args)

    def __get__(self, obj, objtype):
        """Support instance methods."""
        return functools.partial(self.__call__, obj)

    @staticmethod
    def reset():
        for memo in Memoized.caches:
            memo.cache = {}

if __name__ == '__main__':
    import doctest
    doctest.testmod()

编辑：添加测试，并使用weakref.WeakSet。请注意，WeakSet仅适用于2.7（OP正在使用）;对于在2.6中运行的版本，请参阅编辑历史记录。

Answer 2

这是一个明显更快的版本。遗憾的是reset实际上不能完全清除缓存，因为所有实例都将自己的引用本地副本存储到每个函数字典中。虽然你可以让它起作用：

import functools

class Memoized(object):
  """Decorator that caches a function's return value each time it is called.
  If called later with the same arguments, the cached value is returned, and
  not re-evaluated.
  """

  __cache = {}

  def __init__(self, func):
    self.func = func
    key = (func.__module__, func.__name__)
    if key not in self.__cache:
      self.__cache[key] = {}
    self.mycache = self.__cache[key]

  def __call__(self, *args):
    try:
      return self.mycache[args]
    except KeyError:
      value = self.func(*args)
      self.mycache[args] = value
      return value
    except TypeError:
      # uncachable -- for instance, passing a list as an argument.
      # Better to not cache than to blow up entirely.
      return self.func(*args)

  def __get__(self, obj, objtype):
    """Support instance methods."""
    return functools.partial(self.__call__, obj)

  @classmethod
  def reset(cls):
    for v in cls.__cache.itervalues():
      v.clear()

可以做些什么来加速这个memoization装饰器？

2 个答案: