python 来自http://code.activestate.com/recipes/498245-lru-and-lfu-cache-decorators/

Posted 2021-05-10

def lfu_cache(maxsize=100):
    '''Least-frequenty-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/Least_Frequently_Used
    @Bradley-Ayers : I slightly modified lfu_cache to avoid a problem when
    everything in the cache has been called at least twice. 
    In this scenario, the new function call will be added to the cache, 
    but will be immediately removed (as it has less hits than everything else).



    '''
    def decorating_function(user_function):
        cache = {}                      # mapping of args to results
        use_count = Counter()           # times each key has been accessed
        kwarg_mark = object()             # separate positional and keyword args

        @functools.wraps(user_function)
        def wrapper(*args, **kwargs):
            key = args
            if kwargs:
                key += (kwarg_mark,) + tuple(sorted(kwargs.items()))

            # get cache entry or compute if not found
            try:
                result = cache[key]
                use_count[key] += 1
                wrapper.hits += 1
            except KeyError:
                # need to add something to the cache, make room if necessary
                if len(cache) == maxsize:
                    for k, _ in nsmallest(maxsize // 10 or 1,
                                            use_count.iteritems(),
                                            key=itemgetter(1)):
                        del cache[k], use_count[k]
                cache[key] = user_function(*args, **kwargs)
                result = cache[key]
                use_count[key] += 1
                wrapper.misses += 1
            return result

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

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

import collections
import functools

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.
    http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used

    '''
    def decorating_function(user_function):
        cache = collections.OrderedDict()    # order: least recent to most recent

        @functools.wraps(user_function)
        def wrapper(*args, **kwds):
            key = args
            if kwds:
                key += tuple(sorted(kwds.items()))
            try:
                result = cache.pop(key)
                wrapper.hits += 1
            except KeyError:
                result = user_function(*args, **kwds)
                wrapper.misses += 1
                if len(cache) >= maxsize:
                    cache.popitem(0)    # purge least recently used cache entry
            cache[key] = result         # record recent use of this key
            return result
        wrapper.hits = wrapper.misses = 0
        return wrapper
    return decorating_function

# this implementation appears to be about 50% faster (using -m profile and 
# your __main__ method as the performance test):

import time
import heapq

def memoize(maxsize):
    """decorator to 'memoize' a function - caching its results
    @sasa sasa
    """
    def decorating_function(f):
        cache = {}  # map from key to value
        heap = []   # list of keys, in LRU heap
        cursize = 0 # because len() is slow
        def wrapper(*args):
            key = repr(args)
            # performance crap
            _cache=cache
            _heap=heap
            _heappop = heapq.heappop
            _heappush = heapq.heappush
            _time = time.time
            _cursize = cursize
            _maxsize = maxsize
            if not _cache.has_key(key):
                if _cursize == _maxsize:
                    # pop oldest element
                    (_,oldkey) = _heappop(_heap)
                    _cache.pop(oldkey)
                else:
                    _cursize += 1
                # insert this element
                _cache[key] = f(*args)
                _heappush(_heap,(_time(),key))
                wrapper.misses += 1
            else:
                wrapper.hits += 1
        return cache[key]
        wrapper.__doc__ = f.__doc__
        wrapper.__name__ = f.__name__
        wrapper.hits = wrapper.misses = 0
        return wrapper
    return decorating_function

import collections
import functools
from itertools import ifilterfalse
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 ifilterfalse(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


def lfu_cache(maxsize=100):
    '''Least-frequenty-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/Least_Frequently_Used

    '''
    def decorating_function(user_function):
        cache = {}                      # mapping of args to results
        use_count = Counter()           # times each key has been accessed
        kwd_mark = object()             # separate positional and keyword args

        @functools.wraps(user_function)
        def wrapper(*args, **kwds):
            key = args
            if kwds:
                key += (kwd_mark,) + tuple(sorted(kwds.items()))
            use_count[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 frequently used cache entry
                if len(cache) > maxsize:
                    for key, _ in nsmallest(maxsize // 10,
                                            use_count.iteritems(),
                                            key=itemgetter(1)):
                        del cache[key], use_count[key]

            return result

        def clear():
            cache.clear()
            use_count.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))

    print(f.hits, f.misses)

    @lfu_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))

    print(f.hits, f.misses)