[oblio]

This is nice, how would you go about as a performance noob? I can't imagine there's a line in the docs saying "this is slow!".

[akasakahakada]

LineProfiler is the best tool to learn how to write performant Python and do code optimization.

https://github.com/pyutils/line_profiler

You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.

For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.

Doing this for like 2 weeks, basically you can automatically recognize most bad code patterns at a glance.

[dralley]

You should take a look at Scalene - it's even better.

https://github.com/plasma-umass/scalene

[akasakahakada]

Wow this looks sick! Great thanks!

[brahbrah]

So in addition to what akasaka said (another thumbs up for line profiler from me, great tool) this isn’t a problem with linalg.norm being slow. It’s plenty fast, but calling it thousands of separate times in a Python loop will be slow. This is more just about learning how to vectorize properly. If you’re working in numpy land and you’re calling a numpy function in a loop that’s iterating over more than a handful of items, chances are you’re not vectorizing properly

[akasakahakada]

I realized that I should say more specifically about numpy usage.

If you see a piece of code like this, it rings the bell that the person has no idea what he/she is doing:

Bad Pattern:

    my_list = np.array(xxx)
    summed = []
    for row in my_list:
        summed.append(np.sum(row))

Worst Pattern:

    my_list = np.array(xxx)
    
    def get_summed(arr):
        return np.sum(arr)

    summed = []
    for i in range(len(my_list)):
        summed.append(
            get_summed(my_list[i])
        )
        

Perfered:

    # np.array(xxx) is redundant
    summed = np.sum(xxx, axis=1)

Same applies to all numpy operations.