Memoization Handler [duplicate]

This question already has answers here: What is memoization and how can I use it in Python? (14 answers) Closed 4 months ago.

Is it "good practice" to create a class like the one below that can handle the memoization process for you? The benefits of memoization are so great (in some cases, like this one, where it drops from 501003 to 1507 function calls and from 1.409 to 0.006 seconds of CPU time on my computer) that it seems 开发者_运维技巧a class like this would be useful.

However, I've read only negative comments on the usage of eval(). Is this usage of it excusable, given the flexibility this approach offers?

This can save any returned value automatically at the cost of losing side effects. Thanks.

import cProfile

class Memoizer(object):
    """A handler for saving function results."""
    def __init__(self):
        self.memos = dict()
    def memo(self, string):
        if string in self.memos:
            return self.memos[string]
        else:
            self.memos[string] = eval(string)
            self.memo(string)

def factorial(n):
    assert type(n) == int
    if n == 1:
        return 1
    else:
        return n * factorial(n-1) 

# find the factorial of num
num = 500
# this many times
times = 1000

def factorialTwice():
    factorial(num)
    for x in xrange(0, times):
        factorial(num)
    return factorial(num)

def memoizedFactorial():
    handler = Memoizer()
    for x in xrange(0, times):
        handler.memo("factorial(%d)" % num)
    return handler.memo("factorial(%d)" % num)


cProfile.run('factorialTwice()')

cProfile.run('memoizedFactorial()')

---

You can memoize without having to resort to eval.

A (very basic) memoizer:

def memoized(f):
    cache={}
    def ret(*args):
        if args in cache:
            return cache[args]
        else:
            answer=f(*args)
            cache[args]=answer
            return answer
    return ret

@memoized
def fibonacci(n):
    if n==0 or n==1:
        return 1
    else:
        return fibonacci(n-1)+fibonacci(n-2)

print fibonacci(100)

---

eval is often misspelt as evil primarily because the idea of executing "strings" at runtime is fraught with security considerations. Have you escaped the code sufficiently? Quotation marks? And a host of other annoying headaches. Your memoise handler works but it's really not the Python way of doing things. MAK's approach is much more Pythonic. Let's try a few experiments.

I edited up both the versions and made them run just once with 100 as the input. I also moved out the instantiation of Memoizer. Here are the results.

>>> timeit.timeit(memoizedFactorial,number=1000)
0.08526921272277832h
>>> timeit.timeit(foo0.mfactorial,number=1000)
0.000804901123046875

In addition to this, your version necessitates a wrapper around the the function to be memoised which should be written in a string. That's ugly. MAK's solution is clean since the "process of memoisation" is encapsulated in a separate function which can be conveniently applied to any expensive function in an unobtrusive fashion. This is not very Pythonic. I have some details on writing such decorators in my Python tutorial at http://nibrahim.net.in/self-defence/ in case you're interested.