What is lru_cache in functools module in Python?
The functools module in Python
The functools module in Python creates higher-order functions that interact with other functions. The higher-order functions either return other functions or operate on them to broaden the function’s scope without modifying or explicitly defining them.
lru_cache()
Memoization is a caching technique that ensures a function need not run multiple times for the same inputs. Instead, the result of the function is stored in memory the first time it’s run and can be referenced later when necessary.
Note: Refer to What is memoization? to learn more about memoization.
lru_cache() is a decorator that helps in reducing function execution for the same inputs using the memoization technique.
The wrapped method has a cache_info() function that produces a named tuple containing hits, misses, maxsize, and currsize to assess the cache’s efficacy and optimize the maxsize parameter.
Syntax
@lru_cache(maxsize=<max_size>, typed=True/False)
The decorator has two parameters:
-
maxsize: This parameter indicates the maximum number of entries the cache can store before evicting old items. If it’s set to none, the cache will grow indefinitely, and no entries will ever be evicted. This will lead to problems if many entries are cached. -
typed: This is a Boolean parameter. When set toTrue, it indicates the cache will have different entries for different types of function arguments.
Code
from functools import lru_cacheimport timedef fibonacci_without_cache(num):if num < 2: return numreturn fibonacci_without_cache(num - 1) + fibonacci_without_cache(num - 2)@lru_cache(maxsize=12)def fibonacci_with_cache(num):if num < 2: return numreturn fibonacci_without_cache(num - 1) + fibonacci_without_cache(num - 2)start_timer = time.time()print("fibonacci_without_cache(8):", fibonacci_without_cache(8))end_timer = time.time()print("Time taken to calculate fibonacci series without cache:", end_timer - start_timer)start_timer = time.time()print("fibonacci_with_cache(8):", fibonacci_with_cache(8))end_timer = time.time()print("Time taken to calculate fibonacci series with cache:", end_timer - start_timer)print("Cache information of fibonacci_with_cache function:\n",fibonacci_with_cache.cache_info())
Code explanation
- Lines 1–2: We import the
lru_cachedecorator and time module. - Lines 4–6: We define a method to calculate the Fibonacci series called
fibonacci_without_cache. - Lines 8–11: We define a method to calculate the Fibonacci series called
fibonacci_with_cache. This method is decorated with@lru_cachewith the max cache size as12. - Lines 14–17: We calculate the running time of
fibonacci_without_cache()method with the help of thetimemodule. - Lines 20–23: We calculate the running time of
fibonacci_with_cache()method with the help of thetimemodule. - Line 25: We use the
cache_info()method to obtain the cache information forfibonacci_with_cache().
