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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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np.seterr(divide="ignore", invalid="ignore") |
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from ..base_optimizer import BaseOptimizer |
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from ...search import Search |
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def skip_refit_75(i): |
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if i <= 33: |
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return 1 |
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return int((i - 33) ** 0.75) |
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def skip_refit_50(i): |
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if i <= 33: |
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return 1 |
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return int((i - 33) ** 0.5) |
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def skip_refit_25(i): |
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if i <= 33: |
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return 1 |
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return int((i - 33) ** 0.25) |
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def never_skip_refit(i): |
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return 1 |
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skip_retrain_ = { |
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"many": skip_refit_75, |
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"some": skip_refit_50, |
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"few": skip_refit_25, |
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"never": never_skip_refit, |
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} |
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class SBOM(BaseOptimizer, Search): |
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def __init__( |
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self, |
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search_space, |
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max_sample_size=1000000, |
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warm_start_smbo=None, |
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skip_retrain="never", |
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): |
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super().__init__(search_space) |
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self.max_sample_size = max_sample_size |
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self.warm_start_smbo = warm_start_smbo |
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self.skip_retrain = skip_retrain_[skip_retrain] |
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self.X_sample = [] |
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self.Y_sample = [] |
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self._all_possible_pos() |
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if self.warm_start_smbo is not None: |
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(self.X_sample, self.Y_sample) = self.warm_start_smbo |
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def track_X_sample(func): |
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def wrapper(self, *args, **kwargs): |
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pos = func(self, *args, **kwargs) |
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self.X_sample.append(pos) |
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return pos |
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return wrapper |
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def get_random_sample(self): |
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sample_size = self._sample_size() |
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if sample_size > self.all_pos_comb.shape[0]: |
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sample_size = self.all_pos_comb.shape[0] |
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row_sample = np.random.choice( |
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self.all_pos_comb.shape[0], size=(sample_size,), replace=False |
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) |
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return self.all_pos_comb[row_sample] |
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def _sample_size(self): |
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n = self.max_sample_size |
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return int(n * np.tanh(self.all_pos_comb.size / n)) |
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def _all_possible_pos(self): |
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pos_space = [] |
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for dim_ in self.space_dim_size: |
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pos_space.append(np.arange(dim_)) |
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self.n_dim = len(pos_space) |
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self.all_pos_comb = np.array(np.meshgrid(*pos_space)).T.reshape( |
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-1, self.n_dim |
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) |
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@track_X_sample |
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def init_pos(self, pos): |
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super().init_pos(pos) |
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return pos |
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SimonBlanke /
Gradient-Free-Optimizers
