Python benchmark library

Installation

pip install penchmark
pip install penchmark[charts]

Example

Classes Callee, InData

from penchmark import benchmark_and_print, Callee, InData

callees = (
    Callee(callee_name='mul', callee=lambda x: x[0] * x[1]),
    Callee(callee_name='nop', callee=lambda x: x)
)
dataset = (
    InData(name='small-data', data=(2, 1), count_of_call=100000),
    InData(name='big-data', data=(200, 10), count_of_call=1000),
    InData(name='skipped-data', data=(1, 1), count_of_call=0)
)
benchmark_and_print(callees, dataset)

Tuples

from penchmark import benchmark_and_print

callees = (
    ('mul', lambda x: x[0] * x[1]),
    ('nop', lambda x: x)
)
dataset = (
    ('small-data', (2, 1), 100000),
    ('big-data', (200, 10), 1000),
    ('skipped-data', (1, 1), 0)
)
benchmark_and_print(callees, dataset)

Auto generate names of callees

from penchmark import benchmark_and_print

def mul(x): return x[0] * x[1]
def nop(x): return x

dataset = (
    ('small-data', (2, 1), 100000),
    ('big-data', (200, 10), 1000),
    ('skipped-data', (1, 1), 0)
)
benchmark_and_print((mul, nop), dataset)

Markdown result

small-data

callee_name	elapsed	ratio
nop	0.0050	1.0000
mul	0.0080	1.5842

big-data

callee_name	elapsed	ratio
nop	0.0000	1.0000
mul	0.0001	1.7201

Summary

callee_name	mean	median
nop	1.0000	1.0000
mul	1.6521	1.6521

Console mode result

...
benchmark_and_print((mul, nop), dataset, markdown=False)

SMALL-DATA

callee_name      elapsed    ratio
-------------  ---------  -------
nop               0.0050   1.0000
mul               0.0079   1.5944

BIG-DATA

callee_name      elapsed    ratio
-------------  ---------  -------
nop               0.0001   1.0000
mul               0.0001   1.7565

SUMMARY

callee_name      mean    median
-------------  ------  --------
nop            1.0000    1.0000
mul            1.6754    1.6754

Callee with exceptions

from penchmark import benchmark_and_print

def callee_with_exceptions(x):
    if not x:
        raise Exception()

callees = (
    ('callee-with-exceptions', callee_with_exceptions),
    ('callee-without-exceptions', lambda x: None)
)
dataset = (
    ('valid-data', True, 10),
    ('invalid-data', False, 10),
)
benchmark_and_print(callees, dataset)

valid-data

callee_name	elapsed	ratio
callee-without-exceptions	0.00000	1
callee-with-exceptions	0.00000	2.36735

invalid-data

callee_name	elapsed	ratio
callee-without-exceptions	0.00000	1
callee-with-exceptions	ERROR

Summary

callee_name	mean	median
callee-without-exceptions	1	1

Expected using InData

from penchmark import benchmark_and_print, InData

def mul2_1(sequ): return [x * 2 for x in sequ]
def mul2_2(sequ): return [x + x for x in sequ]

dataset = (
    InData(name='small-data', data=(2, 1), count_of_call=100000, expected=[4, 2]),
    InData(name='big-data', data=(200, 10), count_of_call=1000, expected=[400, 20]),
    InData(name='skipped-data', data=(1, 1), count_of_call=0, expected=[2, 2])
)
benchmark_and_print((mul2_1, mul2_2), dataset)

Expected using tuples

from penchmark import benchmark_and_print

def mul2_1(sequ): return [x * 2 for x in sequ]
def mul2_2(sequ): return [x + x for x in sequ]

dataset = (
    ('small-data', (2, 1), 100000, [4, 2]),
    ('big-data', (200, 10), 1000, [400, 20]),
    ('skipped-data', (1, 1), 0, [2, 2])
)
benchmark_and_print((mul2_1, mul2_2), dataset)

small-data

callee_name	elapsed	ratio
mul2_2	0.02114	1
mul2_1	0.02317	1.09603

big-data

callee_name	elapsed	ratio
mul2_2	0.00021	1
mul2_1	0.00022	1.01147

Summary

callee_name	mean	median
mul2_2	1	1
mul2_1	1.05375	1.05375