On 2024-03-31 00:09, marc nicole via Python-list wrote:

I am creating a memoization example with a function that adds up / averages the elements of an array and compares it with the cached ones to retrieve them in case they are already stored.

In addition, I want to store only if the result of the function differs considerably (passes a threshold e.g. 500000 below).

I created an example using a decorator to do so, the results using the decorator is slightly faster than without the memoization which is OK, but
is the logic of the decorator correct ? anybody can tell me ?

My code is attached below:

import time

def memoize(f):
cache = {}

def g(*args):
if args[1] == "avg":
sum_key_arr = sum(list(args[0])) / len(list(args[0]))

'list' will iterate over args[0] to make a list, and 'sum' will iterate
over that list.

It would be simpler to just let 'sum' iterate over args[0].

elif args[1] == "sum":
sum_key_arr = sum(list(args[0]))
if sum_key_arr not in cache:
for (
key,
value,
) in (
cache.items()
): # key in dict cannot be an array so I use the sum of the array as the key

You can't use a list as a key, but you can use a tuple as a key,
provided that the elements of the tuple are also immutable.

if (
abs(sum_key_arr - key) <= 500000
): # threshold is great here so that all values are approximated!
# print('approximated')
return cache[key]
else:
# print('not approximated')
cache[sum_key_arr] = f(args[0], args[1])
return cache[sum_key_arr]

return g

@memoize
def aggregate(dict_list_arr, operation):
if operation == "avg":
return sum(list(dict_list_arr)) / len(list(dict_list_arr))
if operation == "sum":
return sum(list(dict_list_arr))
return None

t = time.time()
for i in range(200, 15000):
res = aggregate(list(range(i)), "avg")

elapsed = time.time() - t
print(res)
print(elapsed)

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On 2024-03-31 09:04, marc nicole wrote:

Thanks for the first comment which I incorporated

but when you say "You can't use a list as a key, but you can use a
tuple as a key,
provided that the elements of the tuple are also immutable."

does it mean  the result of sum of the array is not convenient to use
as key as I do?
Which tuple I should use to refer to the underlying list value as you suggest?

I was suggesting using `tuple` on the argument:

def memoize(f):
     cache = {}

     def g(*args):
         key = tuple(args[0]), args[1]

         if key not in cache:
             cache[key] = f(args[0], args[1])

         return cache[key]

     return g

Anything else is good in my code ?

Thanks

Le dim. 31 mars 2024 à 01:44, MRAB via Python-list <[email protected]> a écrit :

On 2024-03-31 00:09, marc nicole via Python-list wrote:
> I am creating a memoization example with a function that adds up
/ averages
> the elements of an array and compares it with the cached ones to
retrieve
> them in case they are already stored.
>
> In addition, I want to store only if the result of the function
differs
> considerably (passes a threshold e.g. 500000 below).
>
> I created an example using a decorator to do so, the results
using the
> decorator is slightly faster than without the memoization which
is OK, but
> is the logic of the decorator correct ? anybody can tell me ?
>
> My code is attached below:
>
>
>
> import time
>
>
> def memoize(f):
>      cache = {}
>
>      def g(*args):
>          if args[1] == "avg":
>              sum_key_arr = sum(list(args[0])) / len(list(args[0]))

'list' will iterate over args[0] to make a list, and 'sum' will
iterate
over that list.

It would be simpler to just let 'sum' iterate over args[0].

>          elif args[1] == "sum":
>              sum_key_arr = sum(list(args[0]))
>          if sum_key_arr not in cache:
>              for (
>                  key,
>                  value,
>              ) in (
>                  cache.items()
>              ):  # key in dict cannot be an array so I use the
sum of the
> array as the key

You can't use a list as a key, but you can use a tuple as a key,
provided that the elements of the tuple are also immutable.

>                  if (
>                      abs(sum_key_arr - key) <= 500000
>                  ):  # threshold is great here so that all
values are
> approximated!
>                      # print('approximated')
>                      return cache[key]
>              else:
>                  # print('not approximated')
>                  cache[sum_key_arr] = f(args[0], args[1])
>          return cache[sum_key_arr]
>
>      return g
>
>
> @memoize
> def aggregate(dict_list_arr, operation):
>      if operation == "avg":
>          return sum(list(dict_list_arr)) / len(list(dict_list_arr))
>      if operation == "sum":
>          return sum(list(dict_list_arr))
>      return None
>
>
> t = time.time()
> for i in range(200, 15000):
>      res = aggregate(list(range(i)), "avg")
>
> elapsed = time.time() - t
> print(res)
> print(elapsed)

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