def next(self):
"""Return the next point to evaluate according to acquisition
function. Returns a subspace index, the index in the given subspace
and the corresponding learner parameters in a dict which is
directly unpackable. """
for i, (mmap, grid, membah) in enumerate(zip(self.maps, self.grids, self.members)):
# don't do anything fancy if we haven't sampled each classifier
# at least once or twice, depending
if len(grid) == 1:
# placeholder params, just send nothing
if membah.ndata < 1:
return 0, i, {}
else:
if membah.ndata < 2:
grid_i = np.random.choice(len(grid))
return grid_i, i, self.raw_to_learner_params(i, grid_i)
# get everybody's posteriors
posteriors = [m.posterior(g) for m, g in zip(self.members, self.grids)]
mus = np.concatenate([p[0] for p in posteriors])
s2s = np.concatenate([p[1] for p in posteriors])
# pick the parameters maximizing the acquisition function
acq = ei.expected_improvement(mus, s2s, self.scores)
# handle cases where all acq are the same
global_i = util.eq_rand_idx(acq, np.max(acq))
member_i, grid_i = self.which_member(global_i)
learner_params = self.raw_to_learner_params(member_i, grid_i)
return grid_i, member_i, learner_params
def next(self):
'''Return the next point to evaluate according to acquisition
function. Returns a subspace index, the index in the given subspace
and the corresponding learner parameters in a dict which is
directly unpackable. '''
if self.gp.ndata < 2:
# not enough points pick random parameters
grid_i = np.random.choice(len(self.space))
else:
# optimize the model
optimization.optimize_random_start(self.gp, self.gp_priors)
# pick the parameters maximizing the acquisition function
mu, var = self.gp.posterior(self.space)
acq = ei.expected_improvement(mu, var, self.gp.data[1])
grid_i = util.eq_rand_idx(acq, np.max(acq))
learner_i, learner_params = self.raw_to_learner_params(
self.space[grid_i])
return grid_i, learner_i, learner_params