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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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from ..base_optimizer import BaseOptimizer |
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from ...search import Search |
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from .sampling import InitialSampler |
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import numpy as np |
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from itertools import compress |
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np.seterr(divide="ignore", invalid="ignore") |
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class SMBO(BaseOptimizer, Search): |
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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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warm_start_smbo=None, |
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init_sample_size=10000000, |
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sampling={"random": 1000000}, |
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warnings=100000000, |
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): |
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super().__init__(search_space, initialize) |
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self.warm_start_smbo = warm_start_smbo |
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self.sampling = sampling |
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self.warnings = warnings |
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self.sampler = InitialSampler(self.conv, init_sample_size) |
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if self.warnings: |
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self.memory_warning(init_sample_size) |
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def init_position_combinations(self): |
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self.X_sample = [] |
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self.Y_sample = [] |
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def init_warm_start_smbo(self): |
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if self.warm_start_smbo is not None: |
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# filter out nan and inf |
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warm_start_smbo = self.warm_start_smbo[ |
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~self.warm_start_smbo.isin([np.nan, np.inf, -np.inf]).any(1) |
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] |
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X_sample_values = warm_start_smbo[self.conv.para_names].values |
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Y_sample = warm_start_smbo["score"].values |
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self.X_sample = self.conv.values2positions(X_sample_values) |
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self.Y_sample = list(Y_sample) |
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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 _sampling(self, all_pos_comb): |
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if self.sampling is False: |
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return all_pos_comb |
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elif "random" in self.sampling: |
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return self.random_sampling(all_pos_comb) |
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def random_sampling(self, pos_comb): |
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n_samples = self.sampling["random"] |
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n_pos_comb = pos_comb.shape[0] |
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if n_pos_comb <= n_samples: |
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return pos_comb |
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else: |
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_idx_sample = np.random.choice(n_pos_comb, n_samples, replace=False) |
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pos_comb_sampled = pos_comb[_idx_sample, :] |
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return pos_comb_sampled |
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def _all_possible_pos(self): |
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pos_space = self.sampler.get_pos_space() |
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# print("pos_space", pos_space) |
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n_dim = len(pos_space) |
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return np.array(np.meshgrid(*pos_space)).T.reshape(-1, n_dim) |
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def memory_warning(self, init_sample_size): |
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if ( |
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self.conv.search_space_size > self.warnings |
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and init_sample_size > self.warnings |
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): |
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warning_message0 = "\n Warning:" |
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warning_message1 = ( |
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"\n search space size of " |
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+ str(self.conv.search_space_size) |
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+ " exceeding recommended limit." |
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) |
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warning_message3 = "\n Reduce search space size for better performance." |
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print(warning_message0 + warning_message1 + warning_message3) |
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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
