python - Should I use `random.seed` or `numpy.random.seed` to control random number generation in `scikit-learn`?

53
votes

I'm using scikit-learn and numpy and I want to set the global seed so that my work is reproducible.

Should I use numpy.random.seed or random.seed?

From the link in the comments, I understand that they are different, and that the numpy version is not thread-safe. I want to know specifically which one to use to create IPython notebooks for data analysis. Some of the algorithms from scikit-learn involve generating random numbers, and I want to be sure that the notebook shows the same results on every run.

1
pythonnumpyrandomscikit-learnrandom-seed
for using np.random.seed() you won't need to import anything, but for using random.seed() you will need to import the random module - ZdaR
Please DO NOT set the global seed, this is unsafe. You can create your own Random object and set its seed instead. Read the last comment by Muhammad Alkarouri in this question for a safer workaround: stackoverflow.com/a/3717456/1524913 - jeromej
@Leb thanks for the link, but it's not clear which one I should use in my case. I edited the question. - shadowtalker
@JeromeJ it's not clear how to use color_rnd as per that example. If I run color_rnd.seed(1234), will functions like sklearn.cross_validation.KFold "know" to use it instead of whatever RNG it normally uses? - shadowtalker
They may not if they rely on random directly sadly. My point was, at least then. whenever you type code, avoid to use random itself directly. I'm not sure what to do in your scenario, that's a bit of a bummer. Maybe a decorator but I think you'd have to tinker with the function context but I'm not 100% sure, I'd have to have a deeper look at it to be sure. - jeromej

1 Answers

47
votes

Should I use np.random.seed or random.seed?

That depends on whether in your code you are using numpy's random number generator or the one in random.

The random number generators in numpy.random and random have totally separate internal states, so numpy.random.seed() will not affect the random sequences produced by random.random(), and likewise random.seed() will not affect numpy.random.randn() etc. If you are using both random and numpy.random in your code then you will need to separately set the seeds for both.

Update

Your question seems to be specifically about scikit-learn's random number generators. As far as I can tell, scikit-learn uses numpy.random throughout, so you should use np.random.seed() rather than random.seed().

One important caveat is that np.random is not threadsafe - if you set a global seed, then launch several subprocesses and generate random numbers within them using np.random, each subprocess will inherit the RNG state from its parent, meaning that you will get identical random variates in each subprocess. The usual way around this problem is to pass a different seed (or numpy.random.Random instance) to each subprocess, such that each one has a separate local RNG state.

Since some parts of scikit-learn can run in parallel using joblib, you will see that some classes and functions have an option to pass either a seed or an np.random.RandomState instance (e.g. the random_state= parameter to sklearn.decomposition.MiniBatchSparsePCA). I tend to use a single global seed for a script, then generate new random seeds based on the global seed for any parallel functions.