bibliothèque de mémorisation pour python 2.7
Je vois que python 3.2 a la mémorisation comme décorateur dans la bibliothèque functools. http://docs.python.org/py3k/library/functools.html#functools.lru_cache =
Malheureusement, il n'est pas encore rétroporté à 2,7. Y a-t-il une raison spécifique pour laquelle il n'est pas disponible en 2.7? Existe-t-il une bibliothèque tierce offrant la même fonctionnalité ou dois-je écrire la mienne?
Y a-t-il une raison spécifique pour laquelle il n'est pas disponible en 2.7?
@ Nirk a déjà fourni la raison: malheureusement, la ligne 2.x ne reçoit que des corrections de bugs, et de nouvelles fonctionnalités sont développées pour 3.x uniquement.
Existe-t-il une bibliothèque tierce offrant la même fonctionnalité?
repoze.lru est une implémentation de cache LRU pour Python 2.6, Python 2.7 et Python 3.2.
La documentation et le code source sont disponibles sur GitHub .
Utilisation simple:
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)
Il existe un backport du module functools de Python 3.2.3 à utiliser avec Python 2.7 et PyPy : functools32 .
Il comprend le lru_cache décorateur.
J'étais dans la même situation et j'ai été forcé de le mettre en œuvre par moi-même. Il y avait aussi quelques autres problèmes avec l'implémentation python 3.x:
- Le principal problème n'est pas d'activer un cache séparé pour chaque instance (au cas où la fonction mise en cache est une méthode d'instance). Cela signifie que si je mets une taille maximale de 100 dans le cache et que j'ai 100 instances, si toutes sont également actives, la mise en cache ne fera rien.
- De plus, si vous exécutez clear_cache - il efface le cache pour toutes les instances.
- La deuxième chose principale, c'est que je voulais une fonction de timeout pour effacer le cache toutes les X secondes.
Implémentation de la fonction lru_cache pour python 2.7:
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)))
Code non valable:
#!/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 __== '__main__':
unittest.main()
http://www.python.org/download/releases/3.2.3/
Depuis la version finale de Python 2.7, la ligne 2.x ne recevra que des corrections de bugs, et de nouvelles fonctionnalités sont développées pour 3.x uniquement.
Python 2.7 a quelques fonctionnalités de 3.1 mais lru_cache a été ajouté en 3.2
Comme identifié dans les commentaires, http://code.activestate.com/recipes/578078-py26-and-py30-backport-of-python-33s-lru-cache/ est une solution potentielle