> > 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. Ciao, Tiziano ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
