I have a function with a lot of random.choice, random.choices, random.randint, random.uniform etc functions and i have to put random.seed before them all. I use python 3.8.6, is there anyway to keep a seed initialized just in the function or atleast a way to toggle it instead of doing it every time?

CodePudding user response：

It sounds like you have a misconception about how PRNGs work. They are generators (it's right there in the name!) which produce a sequence of values that are deterministic but are constructed in an attempt to be indistinguishable from true randomness based on statistical tests. In essence, they attempt to pass a Turing test/imitation game for randomness. They do this by maintaining an internal state of bits, which gets updated via a deterministic algorithm with every call to the generator. The only role a seed is supposed to play is to set the initial internal state so that separate runs create reproducible sequences. Repeating the seed for separate runs can be useful for debugging and for playing tricks to reduce the variability of some classes of estimators in Monte Carlo simulation.

All PRNGs eventually cycle. Since the internal state is composed of a finite number of bits and the state update mechanism is deterministic, the entire sequence will repeat from the point where any state duplication occurs. In other words, the output sequence is actually a loop of values. The seed value has nothing to do with the quality of the pseudo-random numbers, that's determined by the state transition algorithm. Conceptually you can think of the seed as just being the entry point to the PRNG's cycle. (Note that this doesn't mean you have a cycle just because you observe the same output, cycling occurs only when the internal state that produces the output repeats. That's why the 1980's and 90's saw an emergence of PRNGs whose state space contained more bits than the output space, allowing duplicate output values as predicted by the birthday problem without having the sequence repeat verbatim from that point on.)

If you mess with a good PRNG by reseeding it multiple times, you're undoing all of the hard work that went into designing an algorithm which passes statistically based Turing tests. Since the seed does not determine the quality of the results, you're invoking additional cost (to spawn a new state from the seed), gaining no statistical benefit, and quite likely harming the ability to pass statistical testing. Don't do that!