cache.py

import collections
import functools
from itertools import filterfalse
from heapq import nsmallest
from operator import itemgetter

class Counter(dict):
    'Mapping where default values are zero'
    def __missing__(self, key):
        return 0

def lru_cache(maxsize=100):
    '''Least-recently-used cache decorator.

    Arguments to the cached function must be hashable.
    Cache performance statistics stored in f.hits and f.misses.
    Clear the cache with f.clear().
    http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used

    '''
    maxqueue = maxsize * 10
    def decorating_function(user_function,
            len=len, iter=iter, tuple=tuple, sorted=sorted, KeyError=KeyError):
        cache = {}                  # mapping of args to results
        queue = collections.deque() # order that keys have been used
        refcount = Counter()        # times each key is in the queue
        sentinel = object()         # marker for looping around the queue
        kwd_mark = object()         # separate positional and keyword args

        # lookup optimizations (ugly but fast)
        queue_append, queue_popleft = queue.append, queue.popleft
        queue_appendleft, queue_pop = queue.appendleft, queue.pop

        @functools.wraps(user_function)
        def wrapper(*args, **kwds):
            # cache key records both positional and keyword args
            key = args
            if kwds:
                key += (kwd_mark,) + tuple(sorted(kwds.items()))

            # record recent use of this key
            queue_append(key)
            refcount[key] += 1

            # get cache entry or compute if not found
            try:
                result = cache[key]
                wrapper.hits += 1
            except KeyError:
                result = user_function(*args, **kwds)
                cache[key] = result
                wrapper.misses += 1

                # purge least recently used cache entry
                if len(cache) > maxsize:
                    key = queue_popleft()
                    refcount[key] -= 1
                    while refcount[key]:
                        key = queue_popleft()
                        refcount[key] -= 1
                    del cache[key], refcount[key]

            # periodically compact the queue by eliminating duplicate keys
            # while preserving order of most recent access
            if len(queue) > maxqueue:
                refcount.clear()
                queue_appendleft(sentinel)
                for key in filterfalse(refcount.__contains__,
                                        iter(queue_pop, sentinel)):
                    queue_appendleft(key)
                    refcount[key] = 1


            return result

        def clear():
            cache.clear()
            queue.clear()
            refcount.clear()
            wrapper.hits = wrapper.misses = 0

        wrapper.hits = wrapper.misses = 0
        wrapper.clear = clear
        return wrapper
    return decorating_function


if __name__ == '__main__':

    @lru_cache(maxsize=20)
    def f(x, y):
        return 3*x+y

    domain = range(5)
    from random import choice
    for i in range(1000):
        r = f(choice(domain), choice(domain))

    f.clear()

    print(f.hits, f.misses)