Seeded Python RNG showing non-deterministic behavior with sets

Question

I'm seeing non-deterministic behavior when trying to select a pseudo-random element from sets, even though the RNG is seeded (example code shown below). Why is this happening, and should I expect other Python data types to show similar behavior?

Notes: I've only tested this on Python 2.7, but it's been reproducible on two different Windows computers.

Similar Issue: The issue at Python random seed not working with Genetic Programming example code may be similar. Based on my testing, my hypothesis is that run-to-run memory allocation differences within the sets is leading to different elements getting picked up for the same RNG state.

So far I haven't found any mention of this kind of caveat/issue in the Python docs for set or random.

Example Code (randTest produces different output run-to-run):

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

It has to do with object instantiation of mutable objects. if I create a set of frozenset it does give a deterministic result;

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])])
>>> 

If I'm not mistaken, CPython uses a (mutable) object's memory location as it's id and as the key for hashing.

So while the contents of the objects will always be the same, it's id will be different;

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

A possible solution would be to subclass a frozen set.