What can be done to speed up this decorator of memories?

Question

What I want is a decorator of memories that:

• can memoize instance methods with keyword arguments and arguments
• has a cache that can be cleared (globally) with one call (compared to the one that uses the cache for each function: python resettable memoization decorator )
• is quite effective

I changed the example I saw and came up with the following:

import functools

class Memoized(object):
  """Decorator that caches a function 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 = {}

My problem is that part of the caching seems to be related to a lot of overhead (e.g. for recursive functions). Using the following function as an example, I can call fib (30) ten times with a non-memoized version in less time than a memoized version.

def fib(n):

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

- ? ( ( ) , , ). "" - .

.

P.S. python 2.7

Answer 1

- , , , :

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

.

import functools

class Memoized(object):
    """Decorator that caches a function 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) : 2.86742401123
• fib (30) : 0.000198125839233

:

• __prefix; , .
• , , , , Memoized dict Memoized. .

.

import functools
import weakref

class Memoized(object):
    """Decorator that caches a function 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()

: weakref.WeakSet. , WeakSet 2.7 ( OP); , 2.6, . .

Answer 2

, . , reset , . :

import functools

class Memoized(object):
  """Decorator that caches a function 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)

  @staticmethod
  def reset():
    for v in self.__cache.itervalues():
      v.clear()