pybenchmarks 2.4

PyBenchmarks

自动创建基准表。

基准测试实用程序通过迭代将一个或多个代码段或函数乘以 通过输入参数或键控变量。它返回一本字典 包含运行时间（所有平台）和内存使用量（仅限Linux） 对于输入变量的每个组合。参数或键控变量 当且仅当它是列表、元组、生成器或范围时迭代。

示例

>>> import numpy as np
>>> from pybenchmarks import benchmark
>>> b = benchmark((np.empty, np.ones), (100, 10000, 1000000),
...               dtype=(int, complex))
empty: 100     dtype=int       1.62 us
ones : 100     dtype=int       3.61 us
empty: 100     dtype=complex   1.70 us
ones : 100     dtype=complex   5.42 us
empty: 10000   dtype=int       1.53 us
ones : 10000   dtype=int       7.53 us
empty: 10000   dtype=complex   2.33 us
ones : 10000   dtype=complex  16.29 us
empty: 1000000 dtype=int       1.87 us
ones : 1000000 dtype=int       1.84 ms
empty: 1000000 dtype=complex   2.19 us
ones : 1000000 dtype=complex   4.20 ms

>>> b = benchmark(['np.empty(n, dtype=dtype)', 'np.ones(n, dtype=dtype)'],
...               dtype=(int, complex), n=(100, 10000, 1000000),
...               setup='from __main__ import np')
'np.empty(n, dt...: dtype=int     n=100       1.36 us
'np.ones(n, dty...: dtype=int     n=100       2.83 us
'np.empty(n, dt...: dtype=complex n=100       1.44 us
'np.ones(n, dty...: dtype=complex n=100       3.50 us
'np.empty(n, dt...: dtype=int     n=10000     1.22 us
'np.ones(n, dty...: dtype=int     n=10000     7.05 us
'np.empty(n, dt...: dtype=complex n=10000     1.35 us
'np.ones(n, dty...: dtype=complex n=10000    23.78 us
'np.empty(n, dt...: dtype=int     n=1000000   1.47 us
'np.ones(n, dty...: dtype=int     n=1000000   2.04 ms
'np.empty(n, dt...: dtype=complex n=1000000   2.91 us
'np.ones(n, dty...: dtype=complex n=1000000   4.26 ms

>>> import time
>>> benchmark(time.sleep, (1, 2, 3))
1   1.00 s
2   2.00 s
3   3.00 s

>>> shapes = (100, 10000, 1000000)
>>> setup = """
... import numpy as np
... a = np.random.random_sample(shape)
... """
>>> b = benchmark('np.dot(a, a)', shape=shapes, setup=setup)
shape=100       1.38 us
shape=10000     6.33 us
shape=1000000 855.44 us

>>> shapes = (10, 100, 1000)
>>> setup="""
... import numpy as np
... a = np.random.random_sample((m, n))
... b = np.random.random_sample(n)
... """
>>> b = benchmark('np.dot(a, b)', m=shapes, n=shapes, setup=setup)
m=10   n=10     1.08 us
m=100  n=10     1.61 us
m=1000 n=10     6.91 us
m=10   n=100    1.48 us
m=100  n=100    4.16 us
m=1000 n=100   20.69 us
m=10   n=1000   4.42 us
m=100  n=1000  39.23 us
m=1000 n=1000 931.04 us

>>> def f(x, n, start=1):
...     out = start
...     for i in range(n):
...         out *= x
...     return out
>>> b = benchmark(f, np.full(10, 2), xrange(10), start=2.)
0   1.09 us
1   4.15 us
2   5.25 us
3   5.53 us
4  13.10 us
5   9.23 us
6   9.69 us
7  10.46 us
8  13.03 us
9  10.77 us