Python Functools - lru_cache()

The functools module in Python deals with higher-order functions, that is, functions operating on(taking as arguments) or returning functions and other such callable objects. The functools module provides a wide array of methods such as cached_property(func), cmp_to_key(func), lru_cache(func), wraps(func), etc. It is worth noting that these methods take functions as arguments.

lru_cache()

lru_cache() is one such function in functools module which helps in reducing the execution time of the function by using memoization technique.

Syntax: @lru_cache(maxsize=128, typed=False) Parameters: maxsize:This parameter sets the size of the cache, the cache can store upto maxsize most recent function calls, if maxsize is set to None, the LRU feature will be disabled and the cache can grow without any limitations typed: If typed is set to True, function arguments of different types will be cached separately. For example, f(3) and f(3.0) will be treated as distinct calls with distinct results and they will be stored in two separate entries in the cache

Example:1 Python3 1==

from functools import lru_cache
import time


# Function that computes Fibonacci 
# numbers without lru_cache
def fib_without_cache(n):
    if n < 2:
        return n
    return fib_without_cache(n-1) + fib_without_cache(n-2)
    
# Execution start time
begin = time.time()
fib_without_cache(30)

# Execution end time
end = time.time()

print("Time taken to execute the\
function without lru_cache is", end-begin)

# Function that computes Fibonacci
# numbers with lru_cache
@lru_cache(maxsize = 128)
def fib_with_cache(n):
    if n < 2:
        return n
    return fib_with_cache(n-1) + fib_with_cache(n-2)
    
begin = time.time()
fib_with_cache(30)
end = time.time()

print("Time taken to execute the \
function with lru_cache is", end-begin)

Output:

Time taken to execute the function without lru_cache is 0.4448213577270508 Time taken to execute the function with lru_cache is 2.8371810913085938e-05

Example 2: Python3

from functools import lru_cache

@lru_cache(maxsize = 100)
def count_vowels(sentence):
    sentence = sentence.casefold()
    return sum(sentence.count(vowel) for vowel in 'aeiou')
    
print(count_vowels("Welcome to Geeksforgeeks"))

Output:

9