时间的奇怪结果

Question

I tried to repeat the functionality of IPython %time, but for some strange reason, results of testing of some function are horrific.

IPython:

In [11]: from random import shuffle
   ....: import numpy as np
   ....: def numpy_seq_el_rank(seq, el):
   ....:     return sum(seq < el)
   ....:
   ....: seq = np.array(xrange(10000))
   ....: shuffle(seq)
   ....:

In [12]: %timeit numpy_seq_el_rank(seq, 10000//2)
10000 loops, best of 3: 46.1 µs per loop

Python:

from timeit import timeit, repeat

def my_timeit(code, setup, rep, loops):
    result = repeat(code, setup=setup, repeat=rep, number=loops)
    return '%d loops, best of %d: %0.9f sec per loop'%(loops, rep, min(result))

np_setup = '''
from random import shuffle
import numpy as np
def numpy_seq_el_rank(seq, el):
    return sum(seq < el)

seq = np.array(xrange(10000))
shuffle(seq)
'''
np_code = 'numpy_seq_el_rank(seq, 10000//2)'

print 'Numpy seq_el_rank:\n\t%s'%my_timeit(code=np_code, setup=np_setup, rep=3, loops=100)

And its output:

Numpy seq_el_rank:
    100 loops, best of 3: 1.655324947 sec per loop

As you can see, in python i made 100 loops instead 10000 ( and get 35000 times slower result ) as in ipython, because it takes really long time. Can anybody explain why result in python is so slow?

UPD: Here is cProfile.run('my_timeit(code=np_code, setup=np_setup, rep=3, loops=10000)') output:

         30650 function calls in 4.987 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    4.987    4.987 <string>:1(<module>)
        1    0.000    0.000    0.000    0.000 <timeit-src>:2(<module>)
        3    0.001    0.000    4.985    1.662 <timeit-src>:2(inner)
      300    0.006    0.000    4.961    0.017 <timeit-src>:7(numpy_seq_el_rank)
        1    0.000    0.000    4.987    4.987 Lab10.py:47(my_timeit)
        3    0.019    0.006    0.021    0.007 random.py:277(shuffle)
        1    0.000    0.000    0.002    0.002 timeit.py:121(__init__)
        3    0.000    0.000    4.985    1.662 timeit.py:185(timeit)
        1    0.000    0.000    4.985    4.985 timeit.py:208(repeat)
        1    0.000    0.000    4.987    4.987 timeit.py:239(repeat)
        2    0.000    0.000    0.000    0.000 timeit.py:90(reindent)
        3    0.002    0.001    0.002    0.001 {compile}
        3    0.000    0.000    0.000    0.000 {gc.disable}
        3    0.000    0.000    0.000    0.000 {gc.enable}
        3    0.000    0.000    0.000    0.000 {gc.isenabled}
        1    0.000    0.000    0.000    0.000 {globals}
        3    0.000    0.000    0.000    0.000 {isinstance}
        3    0.000    0.000    0.000    0.000 {len}
        3    0.000    0.000    0.000    0.000 {method 'append' of 'list' objects}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
    29997    0.001    0.000    0.001    0.000 {method 'random' of '_random.Random' objects}
        2    0.000    0.000    0.000    0.000 {method 'replace' of 'str' objects}
        1    0.000    0.000    0.000    0.000 {min}
        3    0.003    0.001    0.003    0.001 {numpy.core.multiarray.array}
        1    0.000    0.000    0.000    0.000 {range}
      300    4.955    0.017    4.955    0.017 {sum}
        6    0.000    0.000    0.000    0.000 {time.clock}

Answer 1

Well, one issue is that you're misreading the results. ipython is telling you how long it took each of the 10,000 iterations for the set of 10,000 iterations with the lowest total time. The timeit.repeat module is reporting how long the whole round of 100 iterations took (again, for the shortest of three). So the real discrepancy is 46.1 µs per loop (ipython) vs. 16.5 ms per loop (python), still a factor of ~350x difference, but not 35,000x.

You didn't show profiling results for ipython . Is it possible that in your ipython session, you did either from numpy import sum or from numpy import * ? If so, you'd have been timing the numpy.sum (which is optimized for numpy arrays and would run several orders of magnitude faster ), while your python code (which isolated the globals in a way that ipython does not) ran the normal sum (that has to convert all the values to Python int s and sum them).

If you check your profiling output, virtually all of your work is being done in sum ; if that part of your code was sped up by several orders of magnitude, the total time would reduce similarly. That would explain the "real" discrepancy; in the test case linked above, it was a 40x difference, and that was for a smaller array (the smaller the array, the less numpy can "show off") with more complex values (vs. summing 0s and 1s here I believe).

The remainder (if any) is probably an issue of how the code is being eval ed slightly differently, or possibly weirdness with the random shuffle (for consistent tests, you'd want to seed random with a consistent seed to make the "randomness" repeatable) but I doubt that's a difference of more than a few percent.

Answer 2

There could be any number of reasons this code is running slower in one implementation of python than another. One may be optimized differently than another, one may pre-compile certain parts while the other is fully interpreted. The only way to figure out why is to profile your code.

https://docs.python.org/2/library/profile.html

import cProfile

cProfile.run('repeat(code, setup=setup, repeat=rep, number=loops)')

Will give a result similar to

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    0.000    0.000 <stdin>:1(testing)
     1    0.000    0.000    0.000    0.000 <string>:1(<module>)
     1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
     1    0.000    0.000    0.000    0.000 {method 'upper' of 'str' objects}

Which shows you when function calls were made, how many times they were made and how long they took.