Python Resettable Instance Method Memoization Decorator
Solution 1:
Rather than trying to work out the mechanics of your implementation, I've taken the memoized
decorator class from PythonDecoratorLibrary, and have modified it to add reset
. Below is the result; the trick I've used is to add a callable reset
attribute to the decorated function itself.
class memoized2(object):
"""Decorator that caches a function's return value each time it is called.
If called later with the same arguments, the cached value is returned, and
not re-evaluated.
"""
def __init__(self, func):
self.func = func
self.cache = {}
def __call__(self, *args):
try:
return self.cache[args]
except KeyError:
value = self.func(*args)
self.cache[args] = value
return value
except TypeError:
# uncachable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(*args)
def __repr__(self):
"""Return the function's docstring."""
return self.func.__doc__
def __get__(self, obj, objtype):
"""Support instance methods."""
fn = functools.partial(self.__call__, obj)
fn.reset = self._reset
return fn
def _reset(self):
self.cache = {}
class my_class:
@memoized2
def my_func(self, val):
print "in my_func"
time.sleep(2)
return val
c = my_class()
print "should take time"
print c.my_func(55)
print
print "should be instant"
print c.my_func(55)
print
c.my_func.reset()
print "should take time"
print c.my_func(55)
Solution 2:
Building upon the answer to the original question given by @aix I have created a class that I think could improve it. The main feature is that the cached values are stored as a property of the instance whose method is being decorated, hence it is very easy to reset them.
class memoize(object):
def __init__(self, func):
#print "Init"
self.func = func
def __call__(self, *args):
#print "Call"
if not self.func in self.cache:
self.cache[self.func] = {}
try:
return self.cache[self.func][args]
except KeyError:
value = self.func(*args)
self.cache[self.func][args] = value
return value
except TypeError:
# uncachable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(*args)
def __repr__(self):
"""Return the function's docstring."""
return self.func.__doc__
def __get__(self, obj, objtype):
"""Support instance methods."""
#print "Get", obj, objtype
fn = functools.partial(self.__call__, obj)
try:
self.cache = obj.cache
except:
obj.cache = {}
self.cache = obj.cache
#print self.cache
return fn
As an example of usage:
class MyClass(object):
def __init__(self,data):
self.data = data
def update(self,data):
self.data = data
self.cache = {}
@memoize
def func1(self,x):
print "Computing func1"
return "I am func1 of %s. Data is %s. x is %s\n" % (self, self.data, x)
@memoize
def func2(self,x):
print "Computing func2"
return "I am func2 of %s. Data is %s. x is %s\n" % (self, self.data, x)
def func3(self,x):
print "Computing func3"
return "I am func3 of %s. Data is %s. x is %s\n" % (self, self.data, x)
mc1 = MyClass("data1")
mc2 = MyClass("data2")
mc3 = MyClass("data3")
print mc1.func1(1)
print mc1.func1(1)
print mc1.func2(1)
print mc1.func2(1)
print mc1.func3(1)
print mc1.func3(1)
print mc2.func1(1)
print mc2.func1(1)
print mc2.func2(1)
print mc2.func2(1)
print mc2.func3(1)
print mc2.func3(1)
print "Update mc1\n"
mc1.update("data1new")
print mc1.func1(1)
print mc1.func2(1)
print mc1.func3(1)
print mc2.func1(1)
print mc2.func2(1)
print mc2.func3(1)
gets as output:
Computing func1
I am func1 of <__main__.MyClass object at 0x100470fd0>. Data is data1. x is 1
I am func1 of <__main__.MyClass object at 0x100470fd0>. Data is data1. x is 1
Computing func2
I am func2 of <__main__.MyClass object at 0x100470fd0>. Data is data1. x is 1
I am func2 of <__main__.MyClass object at 0x100470fd0>. Data is data1. x is 1
Computing func3
I am func3 of <__main__.MyClass object at 0x100470fd0>. Data is data1. x is 1
Computing func3
I am func3 of <__main__.MyClass object at 0x100470fd0>. Data is data1. x is 1
Computing func1
I am func1 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
I am func1 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
Computing func2
I am func2 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
I am func2 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
Computing func3
I am func3 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
Computing func3
I am func3 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
Update mc1
Computing func1
I am func1 of <__main__.MyClass object at 0x100470fd0>. Data is data1new. x is 1
Computing func2
I am func2 of <__main__.MyClass object at 0x100470fd0>. Data is data1new. x is 1
Computing func3
I am func3 of <__main__.MyClass object at 0x100470fd0>. Data is data1new. x is 1
I am func1 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
I am func2 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
Computing func3
I am func3 of <__main__.MyClass object at 0x100476050>. Data is data2. x is 1
Solution 3:
Well, I would like to point out two performance issues in your code. This is not an answer to your question, but I can't make it a comment. Thanks to @delnan for pointing out that has_key
is deprecated. Instead of:
try:
return self.cache[key]
except KeyError:
self.cache[key] = self.func(*args, **kwargs)
return self.cache[key]
except TypeError:
# uncacheable, so just return calculated value without caching
return self.func(*args, **kwargs)
I would make it this way:
resultDone = False
result = None
try:
if key in self.cache: return self.cache[key]
else:
result = self.func(*args, **kwargs)
resultDone = True
self.cache[key] = result
except TypeError: # unhashable key
pass
if resultDone: return result
else: return self.func(*args, **kwargs)
This avoids: a) try/except KeyError
; b) calling cache[key]
on return; c) calling the function once more on unhashable keys.
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