python - Faster alternatives to numpy.argmax/argmin which is slow -

python - Faster alternatives to numpy.argmax/argmin which is slow -

i using lot of argmin , argmax in python.

unfortunately, function slow.

i have done searching around, , best can find here:

http://lemire.me/blog/archives/2008/12/17/fast-argmax-in-python/

def fastest_argmax(array): array = list( array ) homecoming array.index(max(array))

unfortunately, solution still half fast np.max, , think should able find fast np.max.

x = np.random.randn(10) %timeit np.argmax( x ) 10000 loops, best of 3: 21.8 per loop %timeit fastest_argmax( x ) 10000 loops, best of 3: 20.8 per loop

as note, applying pandas dataframe groupby

e.g.

%timeit grp2[ 'odds' ].agg( [ fastest_argmax ] ) 100 loops, best of 3: 8.8 ms per loop %timeit grp2[ 'odds' ].agg( [ np.argmax ] ) 100 loops, best of 3: 11.6 ms per loop

where info looks this:

grp2[ 'odds' ].head() out[60]: event_id selection_id 104601100 4367029 682508 3.05 682509 3.15 682510 3.25 682511 3.35 5319660 682512 2.04 682513 2.08 682514 2.10 682515 2.12 682516 2.14 5510310 682520 4.10 682521 4.40 682522 4.50 682523 4.80 682524 5.30 5559264 682526 5.00 682527 5.30 682528 5.40 682529 5.50 682530 5.60 5585869 682533 1.96 682534 1.97 682535 1.98 682536 2.02 682537 2.04 6064546 682540 3.00 682541 2.74 682542 2.76 682543 2.96 682544 3.05 104601200 4916112 682548 2.64 682549 2.68 682550 2.70 682551 2.72 682552 2.74 5315859 682557 2.90 682558 2.92 682559 3.05 682560 3.10 682561 3.15 5356995 682564 2.42 682565 2.44 682566 2.48 682567 2.50 682568 2.52 5465225 682573 1.85 682574 1.89 682575 1.91 682576 1.93 682577 1.94 5773661 682588 5.00 682589 4.40 682590 4.90 682591 5.10 6013187 682592 5.00 682593 4.20 682594 4.30 682595 4.40 682596 4.60 104606300 2489827 683438 4.00 683439 3.90 683440 3.95 683441 4.30 683442 4.40 3602724 683446 2.16 683447 2.32 name: odds, length: 65, dtype: float64

it turns out np.argmax is blazingly fast, only native numpy arrays. foreign data, time spent on conversion:

in [194]: print platform.architecture() ('64bit', 'windowspe') in [5]: x = np.random.rand(10000) in [57]: l=list(x) in [123]: timeit numpy.argmax(x) 100000 loops, best of 3: 6.55 per loop in [122]: timeit numpy.argmax(l) 1000 loops, best of 3: 729 per loop in [134]: timeit numpy.array(l) 1000 loops, best of 3: 716 per loop

i called function "inefficient" because first converts list, iterates through 2 times (effectively, 3 iterations + list construction).

i going suggest iterates once:

def imax(seq): it=iter(seq) im=0 try: m=it.next() except stopiteration: raise valueerror("the sequence empty") i,e in enumerate(it,start=1): if e>m: m=e im=i homecoming im

but, version turns out faster because iterates many times in c, rather python, code. c much faster - considering fact great deal of time spent on conversion, too:

in [158]: timeit imax(x) 1000 loops, best of 3: 883 per loop in [159]: timeit fastest_argmax(x) 1000 loops, best of 3: 575 per loop in [174]: timeit list(x) 1000 loops, best of 3: 316 per loop in [175]: timeit max(l) 1000 loops, best of 3: 256 per loop in [181]: timeit l.index(0.99991619010758348) #the greatest number in case, @ index 92 100000 loops, best of 3: 2.69 per loop

so, key knowledge speeding farther know format info in sequence natively (e.g. whether can omit conversion step or use/write functionality native format).

btw, you're speedup using aggregate(max_fn) instead of agg([max_fn]).

python numpy