Seeded Python RNG showing non-deterministic behavior with sets

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