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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
np.seterr(divide="ignore", invalid="ignore")
from ..base_optimizer import BaseOptimizer
from ...search import Search
class SMBO(BaseOptimizer, Search):
def __init__(
self,
search_space,
initialize={"grid": 4, "random": 2, "vertices": 4},
warm_start_smbo=None,
):
super().__init__(search_space, initialize)
self.warm_start_smbo = warm_start_smbo
self.X_sample = []
self.Y_sample = []
self.all_pos_comb = self._all_possible_pos()
def init_warm_start_smbo(self):
if self.warm_start_smbo is not None:
(X_sample_values, Y_sample) = self.warm_start_smbo
self.X_sample = self.conv.values2positions(X_sample_values)
self.Y_sample = list(Y_sample)
def track_X_sample(func):
def wrapper(self, *args, **kwargs):
pos = func(self, *args, **kwargs)
self.X_sample.append(pos)
return pos
return wrapper
def _all_possible_pos(self):
pos_space = []
for dim_ in self.conv.max_positions:
pos_space.append(np.arange(dim_))
n_dim = len(pos_space)
return np.array(np.meshgrid(*pos_space)).T.reshape(-1, n_dim)
@track_X_sample
def init_pos(self, pos):
super().init_pos(pos)
SimonBlanke /
Gradient-Free-Optimizers
