我有一个简单的备忘录，我用它来节省一些时间在昂贵的网络通话。大致上，我的代码如下：
# mem.py
import functools
import time
def memoize(fn):
"""
Decorate a function so that it results are cached in memory.
>>> import random
>>> random.seed(0)
>>> f = lambda x: random.randint(0, 10)
>>> [f(1) for _ in range(10)]
[9, 8, 4, 2, 5, 4, 8, 3, 5, 6]
>>> [f(2) for _ in range(10)]
[9, 5, 3, 8, 6, 2, 10, 10, 8, 9]
>>> g = memoize(f)
>>> [g(1) for _ in range(10)]
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
>>> [g(2) for _ in range(10)]
[8, 8, 8, 8, 8, 8, 8, 8, 8, 8]
"""
cache = {}
@functools.wraps(fn)
def wrapped(*args, **kwargs):
key = args, tuple(sorted(kwargs))
try:
return cache[key]
except KeyError:
cache[key] = fn(*args, **kwargs)
return cache[key]
return wrapped
def network_call(user_id):
time.sleep(1)
return 1
@memoize
def search(user_id):
response = network_call(user_id)
# do stuff to response
return response
我对这段代码进行了测试，我模拟了不同的返回值
network_call()，以确保我在search()中所做的一些修改能够如预期的那样工作。import mock
import mem
@mock.patch('mem.network_call')
def test_search(mock_network_call):
mock_network_call.return_value = 2
assert mem.search(1) == 2
@mock.patch('mem.network_call')
def test_search_2(mock_network_call):
mock_network_call.return_value = 3
assert mem.search(1) == 3
但是，当我运行这些测试时，会得到一个失败，因为
search()返回一个缓存的结果。CAESAR-BAUTISTA:~ caesarbautista$ py.test test_mem.py
============================= test session starts ==============================
platform darwin -- Python 2.7.8 -- py-1.4.26 -- pytest-2.6.4
collected 2 items
test_mem.py .F
=================================== FAILURES ===================================
________________________________ test_search_2 _________________________________
args = (<MagicMock name='network_call' id='4438999312'>,), keywargs = {}
extra_args = [<MagicMock name='network_call' id='4438999312'>]
entered_patchers = [<mock._patch object at 0x108913dd0>]
exc_info = (<class '_pytest.assertion.reinterpret.AssertionError'>, AssertionError(u'assert 2 == 3\n + where 2 = <function search at 0x10893f848>(1)\n + where <function search at 0x10893f848> = mem.search',), <traceback object at 0x1089502d8>)
patching = <mock._patch object at 0x108913dd0>
arg = <MagicMock name='network_call' id='4438999312'>
@wraps(func)
def patched(*args, **keywargs):
# don't use a with here (backwards compatability with Python 2.4)
extra_args = []
entered_patchers = []
# can't use try...except...finally because of Python 2.4
# compatibility
exc_info = tuple()
try:
try:
for patching in patched.patchings:
arg = patching.__enter__()
entered_patchers.append(patching)
if patching.attribute_name is not None:
keywargs.update(arg)
elif patching.new is DEFAULT:
extra_args.append(arg)
args += tuple(extra_args)
> return func(*args, **keywargs)
/opt/boxen/homebrew/lib/python2.7/site-packages/mock.py:1201:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
mock_network_call = <MagicMock name='network_call' id='4438999312'>
@mock.patch('mem.network_call')
def test_search_2(mock_network_call):
mock_network_call.return_value = 3
> assert mem.search(1) == 3
E assert 2 == 3
E + where 2 = <function search at 0x10893f848>(1)
E + where <function search at 0x10893f848> = mem.search
test_mem.py:15: AssertionError
====================== 1 failed, 1 passed in 0.03 seconds ======================
有没有方法测试记忆功能？我考虑过一些替代方案，但它们都有缺点。
一种解决方案是模拟
memoize()。我不愿意这样做，因为它将实现细节泄漏给测试。理论上，我应该能够在没有系统其他部分的情况下记忆和取消记忆功能，包括从功能的角度观察测试。另一种解决方案是重写代码以公开修饰函数。也就是说，我可以这样做：
def _search(user_id):
return network_call(user_id)
search = memoize(_search)
但是，这会遇到与上面相同的问题，尽管可能会更糟，因为它不适用于递归函数。
最佳答案：
是否真的需要在功能级别定义您的记忆？
这有效地使记忆化数据成为一个全局变量（就像函数一样，它共享函数的作用域）。
顺便说一下，这就是为什么你在测试它时遇到困难的原因！
那么，把它包装成一个物体怎么样？
import functools
import time
def memoize(meth):
@functools.wraps(meth)
def wrapped(self, *args, **kwargs):
# Prepare and get reference to cache
attr = "_memo_{0}".format(meth.__name__)
if not hasattr(self, attr):
setattr(self, attr, {})
cache = getattr(self, attr)
# Actual caching
key = args, tuple(sorted(kwargs))
try:
return cache[key]
except KeyError:
cache[key] = meth(self, *args, **kwargs)
return cache[key]
return wrapped
def network_call(user_id):
print "Was called with: %s" % user_id
return 1
class NetworkEngine(object):
@memoize
def search(self, user_id):
return network_call(user_id)
if __name__ == "__main__":
e = NetworkEngine()
for v in [1,1,2]:
e.search(v)
NetworkEngine().search(1)
产量：
Was called with: 1
Was called with: 2
Was called with: 1
换句话说，
NetworkEngine的每个实例都有自己的缓存。只需重用同一个缓存来共享一个缓存，或者实例化一个新的缓存来获取一个新的缓存。在测试代码中，您将使用：
@mock.patch('mem.network_call')
def test_search(mock_network_call):
mock_network_call.return_value = 2
assert mem.NetworkEngine().search(1) == 2
