种子Python RNG显示带有集合的非确定性行为

Question

尝试从集合中选择伪随机元素时，我看到了不确定的行为，即使已为RNG注入了种子（如下所示的示例代码）。为什么会发生这种情况，我是否应该期望其他Python数据类型显示类似的行为？

注意：我仅在Python 2.7上进行了测试，但是在两台不同的Windows计算机上都可以重现。

相似的问题：Python随机种子无法与遗传编程示例代码一起使用时的问题可能相似。根据我的测试，我的假设是，集合之间的运行间内存分配差异导致针对同一RNG状态拾取不同的元素。

迄今为止，我还没有在Python文档中找到关于set或random的这种警告/问题。

示例代码（randTest生成不同的运行结果）：

import random

''' Class contains a large set of pseudo-random numbers. '''
class bigSet:
    def __init__(self):
        self.a = set()
        for n in range(2000):
            self.a.add(random.random())
        return


''' Main test function. '''
def randTest():
    ''' Seed the PRNG. '''
    random.seed(0)

    ''' Create sets of bigSet elements, presumably many memory allocations. ''' 
    b = set()
    for n in range (2000):
        b.add(bigSet())

    ''' Pick a random value from a random bigSet. Would have expected this to be deterministic. '''    
    c = random.sample(b,1)[0]
    print('randVal: ' + str(random.random()))           #This value is always the same
    print('setSample: ' + str(random.sample(c.a,1)[0])) #This value can change run-to-run
    return

Answer 1

它与可变对象的对象实例化有关。如果我创建一个set它确实frozenset会给出确定性结果；

Python 2.7.11 (default, Jan  9 2016, 15:47:04) 
[GCC 4.2.1 Compatible FreeBSD Clang 3.4.1 (tags/RELEASE_34/dot1-final 208032)] on freebsd10
Type "help", "copyright", "credits" or "license" for more information.
>>> import random
>>> random.seed(0)
>>> set(frozenset(random.random() for i in range(5)) for j in range(5))
set([frozenset([0.7298317482601286, 0.3101475693193326, 0.8988382879679935, 0.47214271545271336, 0.6839839319154413]), frozenset([0.5833820394550312, 0.4765969541523558, 0.4049341374504143, 0.30331272607892745, 0.7837985890347726]), frozenset([0.7558042041572239, 0.5046868558173903, 0.9081128851953352, 0.28183784439970383, 0.6183689966753316]), frozenset([0.420571580830845, 0.25891675029296335, 0.7579544029403025, 0.8444218515250481, 0.5112747213686085]), frozenset([0.9097462559682401, 0.8102172359965896, 0.9021659504395827, 0.9827854760376531, 0.25050634136244054])])
>>> random.seed(0)
>>> set(frozenset(random.random() for i in range(5)) for j in range(5))
set([frozenset([0.7298317482601286, 0.3101475693193326, 0.8988382879679935, 0.47214271545271336, 0.6839839319154413]), frozenset([0.5833820394550312, 0.4765969541523558, 0.4049341374504143, 0.30331272607892745, 0.7837985890347726]), frozenset([0.7558042041572239, 0.5046868558173903, 0.9081128851953352, 0.28183784439970383, 0.6183689966753316]), frozenset([0.420571580830845, 0.25891675029296335, 0.7579544029403025, 0.8444218515250481, 0.5112747213686085]), frozenset([0.9097462559682401, 0.8102172359965896, 0.9021659504395827, 0.9827854760376531, 0.25050634136244054])])
>>> 

如果我没记错的话，CPython 使用（可变）对象的内存位置作为它的 id 和散列的键。

因此，虽然对象的内容始终相同，但它的 id 会不同；

In [13]: random.seed(0)

In [14]: k = set()

In [15]: for n in range (20):
    k.add(bigSet())
   ....:     

In [16]: for x in k:
    print(id(x))
   ....:     
34856629808
34856629864
34856631936
34856630424
34856629920
34856631992
34856630480
34856629976
34856632048
34856631040
34856630536
34856632104
34856630032
34856630592
34856630088
34856632160
34856629752
34856629696
34856630760
34856630256

In [17]: random.seed(0)

In [18]: k = set()

In [19]: for n in range (20):
   ....:         k.add(bigSet())
   ....:     

In [20]: for x in k:
   ....:         print(id(x))
   ....:     
34484534800
34856629808
34484534856
34856629864
34856631936
34856630424
34856629920
34856631992
34484534968
34856629976
34856630480
34856632048
34856631040
34484535024
34484535080
34484535136
34856632216
34484534688
34484534912
34484534744

一个可能的解决方案是对冻结集进行子类化。