On 05/08/2012 11:03 PM, Tiziano Zito wrote: >>> is there a point where generic numpy.random gets explicitly seeded >>> upon sklearn import? >> No, and I don't think that this is desirable: it would be a weird side >> effect of importing the scikit. It might be interesting to seed the >> global RNG in the tests, but I have found such an approach inefficient, >> as the order in which the tests can be executed is not reproducible. > I concur with Gaël here. I think one good approach is to seed the > RNG for every single unittest. This gets rid of the ordering > problem, and it makes it possible to re-run just the failing test > with a fixed seed. With MDP we use the same seed picked at random at > the beginning of the testing session for every single test. > As far as I know, I changed all the unittests so that each one set's a separate RNG seed. There might be two or three left that set a global RNG seed in the test file, but there should be no unset seeds in any of the tests. I did not think about the ordering problem and I agree we should move the seed setting into the individual tests in the view remaining cases.
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