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functools for cache

functools was introduced from Python3. This library provides the utility for creating higher-order functions in Python. I didn’t have any experience of using functools.

But today I read the useful article and tried lru_cache functionality by myself. It was introduced in Python 3.2. So please upgrade your Python if 3.x is not installed. It must be worth doing.

Run fibonacci

You may have many times to write a function which returns fibonacci number with given sequence index.

def fib(n):
    if n in [1,2]:
        return 1
        return fib(n - 1) + fib(n - 2)

How long it takes to finish fib(35)?

start = time.time()
print("elapsed time: {}".format(time.time() - start))

# elapsed time: 3.272068977355957

Around 3.3 seconds. The elapsed time of the function is growing exponentially by increasing n. You might not be able to wait to finish fib(100)!

You can easily make the speed up by using functools.lru_cache. Just adding annotation.

from functools import lru_cache

def fib_cache(n):
    if n in [1,2]:
        return 1
        return fib_cache(n - 1) + fib_cache(n - 2)

maxsize specifies the number of saved recent calls. The LRU feature performs best when maxsize is a power-of-two. (e.g. 64, 128) So I set 128. If you specify None, the cache grow without bound.

Then try to run the program.

start = time.time()
print("elapsed time with cache: {}".format(time.time() - start))

# elapsed time with cache: 2.5987625122070312e-05
# CacheInfo(hits=32, misses=35, maxsize=128, currsize=35)

It finished in very short time. cache_info() returns the information for checking the usage of cache. Since it shows 32 cache hits, we found we could save 32 function calls in total.

In Python3, we can enable cache feature by just adding lru_cache annotation. Please try it.