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Python Cookbook by David Ascher, Alex Martelli

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Memoizing (Caching) the Return Values of Functions

Credit: Paul Moore

Problem

You have a pure function that is often called with the same arguments (particularly a recursive function) and is slow to compute its results, and you are looking for a simple way to gain substantial performance.

Solution

The key idea behind memoizing is to store a function’s results in a dictionary, keyed by the arguments that produce each result. Of course, this makes sense only for a pure function (i.e., one that yields the same result when called repeatedly with given arguments). It’s easy to memoize a function by hand. For example, using the recursive Fibonacci function:

fib_memo = {}
def fib(n):
    if n < 2: return 1
    if not fib_memo.has_key(n):
        fib_memo[n] = fib(n-1) + fib(n-2)
    return fib_memo[n]

Having to code the memoization inside each function to be memoized, however, is repetitive and interferes with the function’s readability. A good alternative is to encapsulate the memoization mechanics into a class:

class Memoize:
    def _ _init_ _(self, fn):
        self.fn = fn
        self.memo = {}
        self.cacheable = self.misses = self.noncacheable = 0L
    def _ _call_ _(self, *args, **kwds):
        if not kwds:
            self.cacheable += 1
            try: return self.memo[args]
            except KeyError:
                self.misses += 1
                self.memo[args] = self.fn(*args)
                return self.memo[args]
            except TypeError: self.cacheable -= 1
        self.noncacheable += 1
        return self.fn(*args, **kwds)

Using this class to memoize fib, the function definition becomes obvious without caching boilerplate to obscure the algorithm. However, you must assign the Memoize instance to the same name, fib, as the recursive function. Otherwise, the recursive calls bypass the memoizing:

def fib(n):
    if n < 2: return 1
    return fib(n-1) + fib(n-2)
fib = Memoize(fib)

For functions that take mutable arguments, you can sometimes use the cPickle module to memoize them anyway:

class MemoizeMutable:
    def _ _init_ _(self, fn):
        self.fn = fn
        self.memo = {}
    def _ _call_ _(self, *args, **kwds):
        import cPickle
        str = cPickle.dumps(args, 1)+cPickle.dumps(kwds, 1)
        if not self.memo.has_key(str):
            self.memo[str] = self.fn(*args, **kwds)
        return self.memo[str]

Discussion

The Memoize class is instantiated with one argument, a function f, and returns an instance that acts like f but memoizes its arguments and result if the actual arguments to a call are hashable (nonmutable) and positional. Calls with mutable or keyword arguments are counted in the x.noncacheable instance attribute, cacheable calls are counted in the x.cacheable instance attribute, and cache misses in cacheable calls are counted in the x.misses attribute. Unless x.misses and x.noncacheable are low compared to x.cacheable, you’re better off not memoizing the function. So do a few dry runs that are representative of your intended production usage to examine these statistics and decide if memoization is worth using for your specific application.

As we’ve already noted in the recipe’s example of the Memoize class, it is important that the value of fib is replaced by the memoized version. Storing the memoized version elsewhere, as in memoized_fib = Memoize(fib), will not work, because the recursive calls will then call fib directly, bypassing the cache. This is an issue only for recursive functions, but since recursive functions are prime candidates for memoizing, it’s worth keeping in mind.

Obviously, functions to be memoized must be pure (i.e., they must have no side effects and must always return the same value whenever they are called with the same set of arguments). More significantly, the Memoize class (and the inline version above) does not memoize calls that receive mutable arguments, such as len on a list. (Note that you cannot memoize functions that change their mutable arguments because they are not pure functions). MemoizeMutable weakens this constraint a bit and also accepts named arguments, as long as all arguments can be handled by the cPickle module (most types can, but by no means all).

Memoize and friends cannot really check the semantics of the functions you wrap in them. In other words, the notions of “same value” and “same set of arguments” are somewhat vaguely defined in many cases, so take care. Memoize does try to field occasional calls with keyword and mutable arguments (with an interesting mix of checking and try/except), but performance will suffer unless such cases are occasional. As we already noted, Memoize also keeps counts of cacheable calls, noncacheable calls, and misses. This is a bit of overhead, so you may want to rip the count-keeping out for a lighter, faster, simpler version of Memoize once you’ve taken all the measurements you need to convince yourself that memoizing is a good choice for a given function. MemoizeMutable, just to highlight the contrast, does not try to field nonpicklable arguments and uses checking instead of try/except to detect cache misses (but if you have many hits and few misses, try/except can be a bit faster).

One possible enhancement, like for all caching approaches, would be to use weak references (from the weakref standard module), rather than normal references, for the self.memo cache. This may weaken the cache by reducing its hit rate, but as a plus, it avoids keeping objects alive just because the cache is holding on to them. The trade-off crucially depends on the specifics of your application, so be sure to consider it carefully. For details on weak references, see http://python.sourceforge.net/peps/pep-0205.html.

A similar approach could be used to cache results from external processes (i.e., commands). However, in this case it becomes less feasible to ensure that the crucial characteristics of no side effects and the same results for a given set of arguments are in place, so issues naturally arise about more complex procedures, such as time-based cache expiration, LRU disciplines, and so on. All in all, including such extras in this recipe would complicate it enormously without offering substantial benefits for typical cases. The possibilities mentioned here should, however, be kept in mind for those complicated cases in which they might come in handy.

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