sklearn random state not random

Question

I have been playing around with the random state variable from StratifiedKFold in sklearn, but it does not seem to be random. I believe that setting random_state=5, should give me a different testing set then setting random_state=4, but this does not seem to be the case. I have created some crude reproducible code below. First I load my data:

import numpy as np
from sklearn.cross_validation import StratifiedKFold
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target

Then I set random_state=5, for which I store the last values:

skf=StratifiedKFold(n_splits=5,random_state=5)
for (train, test) in skf.split(X,y): full_test_1=test
full_test_1

array([ 40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  90,  91,  92,
        93,  94,  95,  96,  97,  98,  99, 140, 141, 142, 143, 144, 145,
       146, 147, 148, 149])

Doing the same procedure for random_state=4:

skf=StratifiedKFold(n_splits=5,random_state=4)
for (train, test) in skf.split(X,y): full_test_2=test
full_test_2

array([ 40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  90,  91,  92,
        93,  94,  95,  96,  97,  98,  99, 140, 141, 142, 143, 144, 145,
       146, 147, 148, 149])

I can then check that they are equal:

np.array_equal(full_test_1,full_test_2)
True

I do not think that the two random states should be returning the same numbers. Is there a flaw in my logic or code?

Answer 1

From the linked docs

random_state : None, int or RandomState

When shuffle=True, pseudo-random number generator state used for shuffling. If None, use default numpy RNG for shuffling.

You aren't setting shuffle=True in your call to StratifiedKFold, so random_state won't do anything.