Stop modifying global numpy random seed

[mheilman]

In learner.train and learner.cross_validate, the global numpy random seed is set. This seems unnecessary since the randomized algorithms in scikit-learn generally take random seed arguments. Can we remove these?

skll/skll/learner.py

Line 907 in 5c113bc

np.random.seed(rand_seed)

skll/skll/learner.py

Line 1393 in 5c113bc

np.random.seed(rand_seed)

[dan-blanchard]

Thanks for making this issue; this is something I meant to do a long time ago. We should switch to using random state objects like the scikit-learn documentation recommends. (I would provide links, but their site is actually down at the moment.)

[mheilman]

I think this link suffices:
http://c2.com/cgi/wiki?GlobalVariablesAreBad

[dan-blanchard]

Haha, true.

Here's what the scikit-learn page said though:

If your code relies on a random number generator, it should never use functions like numpy.random.random or numpy.random.normal. This approach can lead to repeatability issues in unit tests. Instead, a numpy.random.RandomState object should be used, which is built from a random_state argument passed to the class or function. The function check_random_state, below, can then be used to create a random number generator object.

check_random_state: create a np.random.RandomState object from a parameter random_state.

• If random_state is None or np.random, then a randomly-initialized RandomState object is returned.
• If random_state is an integer, then it is used to seed a new RandomState object.
• If random_state is a RandomState object, then it is passed through.
  For example:

>> from sklearn.utils import check_random_state
>> random_state = 0
>> random_state = check_random_state(random_state)
>> random_state.rand(4)
array([ 0.5488135 ,  0.71518937,  0.60276338,  0.54488318])

[desilinguist]

Addressed by #245.