What is memoization and how can I use it in Python?
PythonMemoizationPython Problem Overview
I just started Python and I've got no idea what memoization is and how to use it. Also, may I have a simplified example?
Python Solutions
Solution 1 - Python
Memoization effectively refers to remembering ("memoization" → "memorandum" → to be remembered) results of method calls based on the method inputs and then returning the remembered result rather than computing the result again. You can think of it as a cache for method results. For further details, see page 387 for the definition in Introduction To Algorithms (3e), Cormen et al.
A simple example for computing factorials using memoization in Python would be something like this:
factorial_memo = {}
def factorial(k):
if k < 2: return 1
if k not in factorial_memo:
factorial_memo[k] = k * factorial(k-1)
return factorial_memo[k]
You can get more complicated and encapsulate the memoization process into a class:
class Memoize:
def __init__(self, f):
self.f = f
self.memo = {}
def __call__(self, *args):
if not args in self.memo:
self.memo[args] = self.f(*args)
#Warning: You may wish to do a deepcopy here if returning objects
return self.memo[args]
Then:
def factorial(k):
if k < 2: return 1
return k * factorial(k - 1)
factorial = Memoize(factorial)
A feature known as "decorators" was added in Python 2.4 which allow you to now simply write the following to accomplish the same thing:
@Memoize
def factorial(k):
if k < 2: return 1
return k * factorial(k - 1)
The Python Decorator Library has a similar decorator called memoized
that is slightly more robust than the Memoize
class shown here.
Solution 2 - Python
functools.cache
decorator:
Python 3.9 released a new function functools.cache
. It caches in memory the result of a functional called with a particular set of arguments, which is memoization. It's easy to use:
import functools
@functools.cache
def fib(num):
if num < 2:
return num
else:
return fib(num-1) + fib(num-2)
This function without the decorator is very slow, try fib(36)
and you will have to wait about ten seconds.
Adding the cache
decorator ensures that if the function has been called recently for a particular value, it will not recompute that value, but use a cached previous result. In this case, it leads to a tremendous speed improvement, while the code is not cluttered with the details of caching.
functools.lru_cache
decorator:
If you need to support older versions of Python, functools.lru_cache
works in Python 3.2+. By default, it only caches the 128 most recently used calls, but you can set the maxsize
to None
to indicate that the cache should never expire:
@functools.lru_cache(maxsize=None)
def fib(num):
# etc
Solution 3 - Python
The other answers cover what it is quite well. I'm not repeating that. Just some points that might be useful to you.
Usually, memoisation is an operation you can apply on any function that computes something (expensive) and returns a value. Because of this, it's often implemented as a decorator. The implementation is straightforward and it would be something like this
memoised_function = memoise(actual_function)
or expressed as a decorator
@memoise
def actual_function(arg1, arg2):
#body
Solution 4 - Python
I've found this extremely useful
from functools import wraps
def memoize(function):
memo = {}
@wraps(function)
def wrapper(*args):
# add the new key to dict if it doesn't exist already
if args not in memo:
memo[args] = function(*args)
return memo[args]
return wrapper
@memoize
def fibonacci(n):
if n < 2: return n
return fibonacci(n - 1) + fibonacci(n - 2)
fibonacci(25)
Solution 5 - Python
Memoization is keeping the results of expensive calculations and returning the cached result rather than continuously recalculating it.
Here's an example:
def doSomeExpensiveCalculation(self, input):
if input not in self.cache:
<do expensive calculation>
self.cache[input] = result
return self.cache[input]
A more complete description can be found in the wikipedia entry on memoization.
Solution 6 - Python
Let's not forget the built-in hasattr
function, for those who want to hand-craft. That way you can keep the mem cache inside the function definition (as opposed to a global).
def fact(n):
if not hasattr(fact, 'mem'):
fact.mem = {1: 1}
if not n in fact.mem:
fact.mem[n] = n * fact(n - 1)
return fact.mem[n]
Solution 7 - Python
Memoization is basically saving the results of past operations done with recursive algorithms in order to reduce the need to traverse the recursion tree if the same calculation is required at a later stage.
see http://scriptbucket.wordpress.com/2012/12/11/introduction-to-memoization/
Fibonacci Memoization example in Python:
fibcache = {}
def fib(num):
if num in fibcache:
return fibcache[num]
else:
fibcache[num] = num if num < 2 else fib(num-1) + fib(num-2)
return fibcache[num]
Solution 8 - Python
Memoization is the conversion of functions into data structures. Usually one wants the conversion to occur incrementally and lazily (on demand of a given domain element--or "key"). In lazy functional languages, this lazy conversion can happen automatically, and thus memoization can be implemented without (explicit) side-effects.
Solution 9 - Python
Well I should answer the first part first: what's memoization?
It's just a method to trade memory for time. Think of Multiplication Table.
Using mutable object as default value in Python is usually considered bad. But if use it wisely, it can actually be useful to implement a memoization
.
Here's an example adapted from http://docs.python.org/2/faq/design.html#why-are-default-values-shared-between-objects
Using a mutable dict
in the function definition, the intermediate computed results can be cached (e.g. when calculating factorial(10)
after calculate factorial(9)
, we can reuse all the intermediate results)
def factorial(n, _cache={1:1}):
try:
return _cache[n]
except IndexError:
_cache[n] = factorial(n-1)*n
return _cache[n]
Solution 10 - Python
Here is a solution that will work with list or dict type arguments without whining:
def memoize(fn):
"""returns a memoized version of any function that can be called
with the same list of arguments.
Usage: foo = memoize(foo)"""
def handle_item(x):
if isinstance(x, dict):
return make_tuple(sorted(x.items()))
elif hasattr(x, '__iter__'):
return make_tuple(x)
else:
return x
def make_tuple(L):
return tuple(handle_item(x) for x in L)
def foo(*args, **kwargs):
items_cache = make_tuple(sorted(kwargs.items()))
args_cache = make_tuple(args)
if (args_cache, items_cache) not in foo.past_calls:
foo.past_calls[(args_cache, items_cache)] = fn(*args,**kwargs)
return foo.past_calls[(args_cache, items_cache)]
foo.past_calls = {}
foo.__name__ = 'memoized_' + fn.__name__
return foo
Note that this approach can be naturally extended to any object by implementing your own hash function as a special case in handle_item. For example, to make this approach work for a function that takes a set as an input argument, you could add to handle_item:
if is_instance(x, set):
return make_tuple(sorted(list(x)))
Solution 11 - Python
Solution that works with both positional and keyword arguments independently of order in which keyword args were passed (using inspect.getargspec):
import inspect
import functools
def memoize(fn):
cache = fn.cache = {}
@functools.wraps(fn)
def memoizer(*args, **kwargs):
kwargs.update(dict(zip(inspect.getargspec(fn).args, args)))
key = tuple(kwargs.get(k, None) for k in inspect.getargspec(fn).args)
if key not in cache:
cache[key] = fn(**kwargs)
return cache[key]
return memoizer
Similar question: https://stackoverflow.com/questions/14621838/identifying-equivalent-varargs-function-calls-for-memoization-in-python
Solution 12 - Python
Just wanted to add to the answers already provided, the Python decorator library has some simple yet useful implementations that can also memoize "unhashable types", unlike functools.lru_cache
.
Solution 13 - Python
cache = {}
def fib(n):
if n <= 1:
return n
else:
if n not in cache:
cache[n] = fib(n-1) + fib(n-2)
return cache[n]
Solution 14 - Python
If speed is a consideration:
@functools.cache
and@functools.lru_cache(maxsize=None)
are equally fast, taking 0.122 seconds (best of 15 runs) to loop a million times on my system- a global cache variable is quite a lot slower, taking 0.180 seconds (best of 15 runs) to loop a million times on my system
- a
self.cache
class variable is a bit slower still, taking 0.214 seconds (best of 15 runs) to loop a million times on my system
The latter two are implemented similar to how it is described in the currently top-voted answer.
This is without memory exhaustion prevention, i.e. I did not add code in the class
or global
methods to limit that cache's size, this is really the barebones implementation. The lru_cache
method has that for free, if you need this.
One open question for me would be how to unit test something that has a functools
decorator. Is it possible to empty the cache somehow? Unit tests seem like they would be cleanest using the class method (where you can instantiate a new class for each test) or, secondarily, the global variable method (since you can do yourimportedmodule.cachevariable = {}
to empty it).