Is your feature request related to a problem? Please describe.
EnsembleOptimizer uses _drop_random_samples() and np.random.choice to determine which data/features are involved in each bag. When running experiments, you can create reproducible ensembles by setting numpy.random.seed. However, when reading experiments, its easy to overlook that this seed is actually used, because it mutates global state, a form of spooky action at a distance. Worse, if someone thinks that it isn't used, deleting np.random.seed(seed) won't cause errors.
Is your feature request related to a problem? Please describe.
EnsembleOptimizeruses_drop_random_samples()andnp.random.choiceto determine which data/features are involved in each bag. When running experiments, you can create reproducible ensembles by settingnumpy.random.seed. However, when reading experiments, its easy to overlook that this seed is actually used, because it mutates global state, a form of spooky action at a distance. Worse, if someone thinks that it isn't used, deletingnp.random.seed(seed)won't cause errors.The solution is to be explicit at least with setting the seed. Relevant Jax philosophy.
Describe the solution you'd like
EnsembleOptimizeraccepts arand_gen: np.random.Generatorobject.Describe alternatives you've considered
EnsembleOptimizeraccepts aseed: intobject.