def update(self, member_i, grid_i, score):
""" Update the model with the `score` obtained for `member_i`
at index `grid_i` in the member's subspace."""
self.scores.append(score)
self.members[member_i].add_data(self.grids[member_i][grid_i], score)
# Optimize the model
optimization.optimize_random_start(self.members[member_i], self.gp_priors)
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
