numpy的随机vs python的默认随机子采样

Question

我观察到python的默认random.sample比numpy的random.choice快得多。从一个长度为100万的数组中取一小部分样本，random.sample比其numpy的对应物快1000倍。

In [1]: import numpy as np

In [2]: import random

In [3]: arr = [x for x in range(1000000)]

In [4]: nparr = np.array(arr)

In [5]: %timeit random.sample(arr, 5)
The slowest run took 5.25 times longer than the fastest. This could mean that an intermediate result is being cached
100000 loops, best of 3: 4.54 µs per loop

In [6]: %timeit np.random.choice(arr, 5)
10 loops, best of 3: 47.7 ms per loop

In [7]: %timeit np.random.choice(nparr, 5)
The slowest run took 6.79 times longer than the fastest. This could mean that an intermediate result is being cached
100000 loops, best of 3: 7.79 µs per loop

尽管来自numpy数组的numpy采样速度相当快，但它比默认随机采样慢。

上述观察是否正确，或者我错过了random.sample和np.random.choice计算的差异？

Answer 1

您在第一次调用numpy.random.choice时看到的只是将列表arr转换为numpy数组的开销。

至于你的第二次电话会议，情况稍差可能是因为numpy.random.choice提供了非均匀采样的能力，也可以在没有替换的情况下进行采样。