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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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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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*args, |
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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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# warnings={"training": 100000, "prediction": 100000000}, |
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**kwargs |
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): |
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super().__init__(*args, **kwargs) |
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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, max_sample_size) |
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# if self.warnings: |
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# self.memory_warning(max_sample_size) |
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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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# filter out elements that are not in search space |
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int_idx_list = [] |
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for para_name in self.conv.para_names: |
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search_data_dim = warm_start_smbo[para_name].values |
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search_space_dim = self.conv.search_space[para_name] |
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int_idx = np.nonzero(np.in1d(search_data_dim, search_space_dim))[0] |
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int_idx_list.append(int_idx) |
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intersec = int_idx_list[0] |
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for int_idx in int_idx_list[1:]: |
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intersec = np.intersect1d(intersec, int_idx) |
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warm_start_smbo_f = warm_start_smbo.iloc[intersec] |
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X_sample_values = warm_start_smbo_f[self.conv.para_names].values |
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Y_sample = warm_start_smbo_f["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(iterate): |
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def wrapper(self, *args, **kwargs): |
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pos = iterate(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 track_y_sample(evaluate): |
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def wrapper(self, score): |
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evaluate(self, score) |
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if np.isnan(score) or np.isinf(score): |
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del self.X_sample[-1] |
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else: |
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self.Y_sample.append(score) |
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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, max_sample_size): |
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if ( |
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self.conv.search_space_size > self.warnings |
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and max_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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return super().init_pos(pos) |
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@BaseOptimizer.track_nth_iter |
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@track_X_sample |
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def iterate(self): |
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return self._propose_location() |
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@track_y_sample |
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def evaluate(self, score_new): |
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self.score_new = score_new |
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self._evaluate_new2current(score_new) |
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self._evaluate_current2best() |
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def _propose_location(self): |
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self._training() |
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exp_imp = self._expected_improvement() |
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index_best = list(exp_imp.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
