用于python 2.7的memoization库
我发现python 3.2在functools库中有memoization作为装饰器。 http://docs.python.org/py3k/library/functools.html#functools.lru_cache
不幸的是，它还没有回到2.7。 有没有什么特定的原因，为什么它不能在2.7？ 有没有第三方库提供相同的function，或者我应该写我自己的？
有没有什么特定的原因，为什么它不能在2.7？
@Nirk已经提供了原因：不幸的是，2.x行只接收错误修正，而新function仅针对3.x开发。
有没有第三方库提供相同的function？
repoze.lru是一个用于Python 2.6，Python 2.7和Python 3.2的LRUcaching实现。
文档和源代码在GitHub上可用 。
简单的用法：
from repoze.lru import lru_cache @lru_cache(maxsize=500) def fib(n): if n < 2: return n return fib(n-1) + fib(n-2)
Python 3.2.3中的functools模块有一个backport，用于Python 2.7和PyPy ： functools32 。
它包含lru_cache装饰器。
我处于同样的情况，并被迫自己执行。 python 3.x还有一些其他的问题：
- 主要的问题是不为每个实例启用一个单独的caching（如果caching的function是实例方法）。 这意味着如果我将最大大小设置为100，并且有100个实例，如果所有的实例都是平等的，那么caching实际上什么也不做。
- 另外，如果您运行clear_cache，则会清除所有实例的caching。
- 第二个主要的是，我想要一个超时function来每隔X秒清除一次caching。
用于python 2.7的函数lru_cache实现：
import time import functools import collections def lru_cache(maxsize = 255, timeout = None): """lru_cache(maxsize = 255, timeout = None) --> returns a decorator which returns an instance (a descriptor). Purpose - This decorator factory will wrap a function / instance method and will supply a caching mechanism to the function. For every given input params it will store the result in a queue of maxsize size, and will return a cached ret_val if the same parameters are passed. Params - maxsize - int, the cache size limit, anything added above that will delete the first values enterred (FIFO). This size is per instance, thus 1000 instances with maxsize of 255, will contain at max 255K elements. - timeout - int / float / None, every n seconds the cache is deleted, regardless of usage. If None - cache will never be refreshed. Notes - If an instance method is wrapped, each instance will have it's own cache and it's own timeout. - The wrapped function will have a cache_clear variable inserted into it and may be called to clear it's specific cache. - The wrapped function will maintain the original function's docstring and name (wraps) - The type of the wrapped function will no longer be that of a function but either an instance of _LRU_Cache_class or a functool.partial type. On Error - No error handling is done, in case an exception is raised - it will permeate up. """ class _LRU_Cache_class(object): def __init__(self, input_func, max_size, timeout): self._input_func = input_func self._max_size = max_size self._timeout = timeout # This will store the cache for this function, format - {caller1 : [OrderedDict1, last_refresh_time1], caller2 : [OrderedDict2, last_refresh_time2]}. # In case of an instance method - the caller is the instance, in case called from a regular function - the caller is None. self._caches_dict = {} def cache_clear(self, caller = None): # Remove the cache for the caller, only if exists: if caller in self._caches_dict: del self._caches_dict[caller] self._caches_dict[caller] = [collections.OrderedDict(), time.time()] def __get__(self, obj, objtype): """ Called for instance methods """ return_func = functools.partial(self._cache_wrapper, obj) return_func.cache_clear = functools.partial(self.cache_clear, obj) # Return the wrapped function and wraps it to maintain the docstring and the name of the original function: return functools.wraps(self._input_func)(return_func) def __call__(self, *args, **kwargs): """ Called for regular functions """ return self._cache_wrapper(None, *args, **kwargs) # Set the cache_clear function in the __call__ operator: __call__.cache_clear = cache_clear def _cache_wrapper(self, caller, *args, **kwargs): # Create a unique key including the types (in order to differentiate between 1 and '1'): kwargs_key = "".join(map(lambda x : str(x) + str(type(kwargs[x])) + str(kwargs[x]), sorted(kwargs))) key = "".join(map(lambda x : str(type(x)) + str(x) , args)) + kwargs_key # Check if caller exists, if not create one: if caller not in self._caches_dict: self._caches_dict[caller] = [collections.OrderedDict(), time.time()] else: # Validate in case the refresh time has passed: if self._timeout != None: if time.time() - self._caches_dict[caller][1] > self._timeout: self.cache_clear(caller) # Check if the key exists, if so - return it: cur_caller_cache_dict = self._caches_dict[caller][0] if key in cur_caller_cache_dict: return cur_caller_cache_dict[key] # Validate we didn't exceed the max_size: if len(cur_caller_cache_dict) >= self._max_size: # Delete the first item in the dict: cur_caller_cache_dict.popitem(False) # Call the function and store the data in the cache (call it with the caller in case it's an instance function - Ternary condition): cur_caller_cache_dict[key] = self._input_func(caller, *args, **kwargs) if caller != None else self._input_func(*args, **kwargs) return cur_caller_cache_dict[key] # Return the decorator wrapping the class (also wraps the instance to maintain the docstring and the name of the original function): return (lambda input_func : functools.wraps(input_func)(_LRU_Cache_class(input_func, maxsize, timeout)))
unit testing代码：
#!/usr/bin/python # -*- coding: utf-8 -*- import time import random import unittest import lru_cache class Test_Decorators(unittest.TestCase): def test_decorator_lru_cache(self): class LRU_Test(object): """class""" def __init__(self): self.num = 0 @lru_cache.lru_cache(maxsize = 10, timeout = 3) def test_method(self, num): """test_method_doc""" self.num += num return self.num @lru_cache.lru_cache(maxsize = 10, timeout = 3) def test_func(num): """test_func_doc""" return num @lru_cache.lru_cache(maxsize = 10, timeout = 3) def test_func_time(num): """test_func_time_doc""" return time.time() @lru_cache.lru_cache(maxsize = 10, timeout = None) def test_func_args(*args, **kwargs): return random.randint(1,10000000) # Init vars: c1 = LRU_Test() c2 = LRU_Test() m1 = c1.test_method m2 = c2.test_method f1 = test_func # Test basic caching functionality: self.assertEqual(m1(1), m1(1)) self.assertEqual(c1.num, 1) # c1.num now equals 1 - once cached, once real self.assertEqual(f1(1), f1(1)) # Test caching is different between instances - once cached, once not cached: self.assertNotEqual(m1(2), m2(2)) self.assertNotEqual(m1(2), m2(2)) # Validate the cache_clear funcionality only on one instance: prev1 = m1(1) prev2 = m2(1) prev3 = f1(1) m1.cache_clear() self.assertNotEqual(m1(1), prev1) self.assertEqual(m2(1), prev2) self.assertEqual(f1(1), prev3) # Validate the docstring and the name are set correctly: self.assertEqual(m1.__doc__, "test_method_doc") self.assertEqual(f1.__doc__, "test_func_doc") self.assertEqual(m1.__name__, "test_method") self.assertEqual(f1.__name__, "test_func") # Test the limit of the cache, cache size is 10, fill 15 vars, the first 5 will be overwritten for each and the other 5 are untouched. Test that: c1.num = 0 c2.num = 10 m1.cache_clear() m2.cache_clear() f1.cache_clear() temp_list = map(lambda i : (test_func_time(i), m1(i), m2(i)), range(15)) for i in range(5, 10): self.assertEqual(temp_list[i], (test_func_time(i), m1(i), m2(i))) for i in range(0, 5): self.assertNotEqual(temp_list[i], (test_func_time(i), m1(i), m2(i))) # With the last run the next 5 vars were overwritten, now it should have only 0..4 and 10..14: for i in range(5, 10): self.assertNotEqual(temp_list[i], (test_func_time(i), m1(i), m2(i))) # Test different vars don't collide: self.assertNotEqual(test_func_args(1), test_func_args('1')) self.assertNotEqual(test_func_args(1.0), test_func_args('1.0')) self.assertNotEqual(test_func_args(1.0), test_func_args(1)) self.assertNotEqual(test_func_args(None), test_func_args('None')) self.assertEqual(test_func_args(test_func), test_func_args(test_func)) self.assertEqual(test_func_args(LRU_Test), test_func_args(LRU_Test)) self.assertEqual(test_func_args(object), test_func_args(object)) self.assertNotEqual(test_func_args(1, num = 1), test_func_args(1, num = '1')) # Test the sorting of kwargs: self.assertEqual(test_func_args(1, aaa = 1, bbb = 2), test_func_args(1, bbb = 2, aaa = 1)) self.assertNotEqual(test_func_args(1, aaa = '1', bbb = 2), test_func_args(1, bbb = 2, aaa = 1)) # Sanity validation of values c1.num = 0 c2.num = 10 m1.cache_clear() m2.cache_clear() f1.cache_clear() self.assertEqual((f1(0), m1(0), m2(0)), (0, 0, 10)) self.assertEqual((f1(0), m1(0), m2(0)), (0, 0, 10)) self.assertEqual((f1(1), m1(1), m2(1)), (1, 1, 11)) self.assertEqual((f1(2), m1(2), m2(2)), (2, 3, 13)) self.assertEqual((f1(2), m1(2), m2(2)), (2, 3, 13)) self.assertEqual((f1(3), m1(3), m2(3)), (3, 6, 16)) self.assertEqual((f1(3), m1(3), m2(3)), (3, 6, 16)) self.assertEqual((f1(4), m1(4), m2(4)), (4, 10, 20)) self.assertEqual((f1(4), m1(4), m2(4)), (4, 10, 20)) # Test timeout - sleep, it should refresh cache, and then check it was cleared: prev_time = test_func_time(0) self.assertEqual(test_func_time(0), prev_time) self.assertEqual(m1(4), 10) self.assertEqual(m2(4), 20) time.sleep(3.5) self.assertNotEqual(test_func_time(0), prev_time) self.assertNotEqual(m1(4), 10) self.assertNotEqual(m2(4), 20) if __name__ == '__main__': unittest.main()
http://www.python.org/download/releases/3.2.3/
自从Python 2.7的最终版本以来，2.x版本将只接收错误修正，而新的function只针对3.x开发。
Python 2.7具有3.1的一些特性，但在3.2中添加了lru_cache
