I'm currently working on a project where I have a class with various expensive methods that I'd like to cache. I want to implement the cache myself, both for exercise as well as that it's special in that it specifically aimed at functions where f(f(x)) == x
is True
(via a dict subclass where d[key] == value and d[value] == key
is True
). This goes kinda deep into python at times, and I'm a bit lost at the moment.
The cache should be attached to the class that the method is defined on and thus I needed to extract the class from the function in the decorator that adds the cache to a function. The problem is that it seems like python does indeed do something else as f = dec(f)
when decorating f
with @dec
.
My test code and the beginning of the cache decorator is:
def bidirectional_cache(function):
"""Function decorator for caching
For functions where f(f(x)) == x is True
Requires hashable args and doesn't support kwargs
"""
parent_instance = getattr(function, "__self__", None)
#print(type(function))
#print(dir(function))
if parent_instance is None:
parent_class = globals()[function.__qualname__.rstrip(f".{function.__name__}")]
elif type(parent_instance) is type:
parent_class = parent_instance
else:
parent_class = parent_instance.__class__
print(parent_class)
...
class A():
N = 0
def __init__(self, n):
self.n = n
def __hash__(self):
return hash(self.n)
def __add__(self, other):
return self.__class__(int(self) + int(other))
def __int__(self):
return self.n
@bidirectional_cache
def test(self):
return f"n = {self.n}"
@bidirectional_cache
@staticmethod
def test_static(a, b):
return a + b
@bidirectional_cache
@classmethod
def test_class(cls, b):
return N + b
When defining A
without the cache decorator and then execute the following calls (REPL session) it gives the outputs as expected:
>>> bidirectional_cache(A.test)
<class '__main__.A'>
>>> bidirectional_cache(A.test_static)
<class '__main__.A'>
>>> bidirectional_cache(A.test_class)
<class '__main__.A'>
>>> a = A(5)
>>> bidirectional_cache(a.test)
<class '__main__.A'>
>>> bidirectional_cache(a.test_static)
<class '__main__.A'>
>>> bidirectional_cache(a.test_class)
<class '__main__.A'>
But if I instead run the class definition with the decorator I always have staticmethod
objects inside the decorator and it breaks because those don't have a __qualname__
. Calling dir
on Ax
, where x
are all the test methods, gives a completely different output as when dir
is called within the decorator.
The question I have is, why is it that @dec
receives a function object that's different from what dec(f)
receives? Is there any way to retrieve the class a function is defined on within the scope of a decorator or would I always have to manually do Ax = dec(x)
?
The __self__
attribute you are trying to access there is not present when the decorator code is run.
The decorator body is called with the method as a parameter when the class body is run - that is before the class itself is created, not to say instances of that class.
The easiest way to get the instance ( self
) in a method decorator is simply to accept it as a parameter in the wrapper function your decorator uses to replace the original method:
def bidirectional_cache(function):
"""Function decorator for caching
For functions where f(f(x)) == x is True
Requires hashable args and doesn't support kwargs
"""
def wrapper(self, *args, **kw):
parent_instance = self
parent_class = parent_instance.__class__
print(parent_class)
...
result = function(self, *args, **kw)
...
return result
return wrapper
(For the method name to be preserved, you should decorate the wrapper
inner function itself with functools.wraps
)
In this model, when the code inside wrapper
is run, you have a living instance of your class - and the self
parameter is the instance - and you can decide whether or not to call the original function based in whatever you want and have stored in cache from previous calls.
So I just noticed that I did in fact not need to attach the cache to the class, making everything way easier. Details are in my comment under @jsbuenos answer. The final solution looks like this:
class BidirectionalDict(dict):
def __setitem__(self, key, value):
super().__setitem__(hash(key), value)
super().__setitem__(value, key)
def __delitem__(self, key):
super().__delitem__(self[key])
super().__delitem__(key)
def bidirectional_cache(function):
"""Function decorator for caching
For functions where f(f(x)) == x is True
Requires hashable args and doesn't support kwargs
"""
cache = BidirectionalDict()
@wraps(function)
def wrapped(*args):
if hash(args) not in cache:
cache[hash(args)] = function(*args)
return cache[hash(args)]
return wrapped
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