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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import numpy as np |
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from ..smb_opt.smbo import SMBO |
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from scipy.spatial.distance import cdist |
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class LipschitzFunction: |
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def __init__(self, position_l): |
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self.position_l = position_l |
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def find_best_slope(self, X_sample, Y_sample): |
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slopes = [] |
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len_sample = len(X_sample) |
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for i in range(len_sample): |
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for j in range(i + 1, len_sample): |
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x_sample1, y_sample1 = X_sample[i], Y_sample[i] |
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x_sample2, y_sample2 = X_sample[j], Y_sample[j] |
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if y_sample1 != y_sample2 and np.prod((x_sample1 - x_sample2)) != 0: |
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slopes.append( |
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abs(y_sample1 - y_sample2) / abs(x_sample1 - x_sample2) |
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) |
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if not slopes: |
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return 1 |
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return np.max(slopes) |
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def calculate(self, X_sample, Y_sample, score_best): |
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lip_c = self.find_best_slope(X_sample, Y_sample) |
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positions_np = np.array(self.position_l) |
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samples_np = np.array(X_sample) |
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pos_dist = cdist(positions_np, samples_np) * lip_c |
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upper_bound_l = pos_dist |
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upper_bound_l += np.array(Y_sample) |
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mx = np.ma.masked_array(upper_bound_l, mask=upper_bound_l == 0) |
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upper_bound_l = mx.min(1).reshape(1, -1).T |
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upper_bound_l[upper_bound_l <= score_best] = -np.inf |
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return upper_bound_l |
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class LipschitzOptimizer(SMBO): |
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name = "Lipschitz Optimizer" |
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_name_ = "lipschitz_optimizer" |
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__name__ = "LipschitzOptimizer" |
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optimizer_type = "sequential" |
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computationally_expensive = True |
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View Code Duplication |
def __init__( |
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self, |
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search_space, |
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initialize={"grid": 4, "random": 2, "vertices": 4}, |
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constraints=[], |
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random_state=None, |
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rand_rest_p=0, |
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nth_process=None, |
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warm_start_smbo=None, |
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max_sample_size=10000000, |
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sampling={"random": 1000000}, |
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replacement=True, |
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): |
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super().__init__( |
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search_space=search_space, |
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initialize=initialize, |
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constraints=constraints, |
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random_state=random_state, |
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rand_rest_p=rand_rest_p, |
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nth_process=nth_process, |
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warm_start_smbo=warm_start_smbo, |
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max_sample_size=max_sample_size, |
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sampling=sampling, |
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replacement=replacement, |
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) |
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def finish_initialization(self): |
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self.all_pos_comb = self._all_possible_pos() |
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return super().finish_initialization() |
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@SMBO.track_new_pos |
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@SMBO.track_X_sample |
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def iterate(self): |
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self.pos_comb = self._sampling(self.all_pos_comb) |
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lip_func = LipschitzFunction(self.pos_comb) |
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upper_bound_l = lip_func.calculate( |
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self.X_sample, self.Y_sample, self.score_best |
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) |
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index_best = list(upper_bound_l.argsort()[::-1]) |
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all_pos_comb_sorted = self.pos_comb[index_best] |
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pos_best = all_pos_comb_sorted[0] |
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return pos_best |
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SimonBlanke /
Gradient-Free-Optimizers
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