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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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""" |
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import numpy as np |
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from ..base_optimizer import BaseOptimizer |
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from ...search import Search |
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from .bayesian_optimization import BayesianOptimizer |
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def sort_list_idx(list_): |
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list_np = np.array(list_) |
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idx_sorted = list(list_np.argsort()[::-1]) |
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return idx_sorted |
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class PowellsMethod(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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iters_p_dim=20, |
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): |
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super().__init__(search_space, initialize) |
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self.iters_p_dim = iters_p_dim |
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self.current_search_dim = -1 |
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def finish_initialization(self): |
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self.nth_iter_ = -1 |
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self.nth_iter_current_dim = 0 |
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def new_dim(self): |
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self.current_search_dim += 1 |
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if self.current_search_dim >= self.conv.n_dimensions: |
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self.current_search_dim = 0 |
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idx_sorted = sort_list_idx(self.scores_valid) |
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self.powells_pos = [self.positions_valid[idx] for idx in idx_sorted][0] |
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self.powells_scores = [self.scores_valid[idx] for idx in idx_sorted][0] |
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self.nth_iter_current_dim = 0 |
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min_pos = [] |
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max_pos = [] |
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center_pos = [] |
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search_space_1D = {} |
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for idx, para_name in enumerate(self.conv.para_names): |
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if self.current_search_dim == idx: |
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# fill with range of values |
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search_space_pos = self.conv.search_space_positions[idx] |
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search_space_1D[para_name] = search_space_pos |
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min_pos.append(int(np.amin(search_space_pos))) |
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max_pos.append(int(np.amax(search_space_pos))) |
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center_pos.append(int(np.median(search_space_pos))) |
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else: |
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# fill with single value |
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search_space_1D[para_name] = np.array([self.powells_pos[idx]]) |
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min_pos.append(self.powells_pos[idx]) |
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max_pos.append(self.powells_pos[idx]) |
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center_pos.append(self.powells_pos[idx]) |
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self.init_positions_ = [min_pos, center_pos, max_pos] |
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self.bayes_opt = BayesianOptimizer( |
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search_space=search_space_1D, initialize={"vertices": 2, "random": 3} |
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) |
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@BaseOptimizer.track_nth_iter |
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def iterate(self): |
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self.nth_iter_ += 1 |
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self.nth_iter_current_dim += 1 |
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modZero = self.nth_iter_ % self.iters_p_dim == 0 |
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# nonZero = self.nth_iter_ != 0 |
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if modZero: |
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self.new_dim() |
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if self.nth_iter_current_dim < 5: |
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pos_new = self.bayes_opt.init_pos( |
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self.bayes_opt.init_positions[self.nth_iter_current_dim] |
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) |
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else: |
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pos_new = self.bayes_opt.iterate() |
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pos_new = self.bayes_opt.conv.position2value(pos_new) |
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return pos_new |
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def evaluate(self, score_new): |
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self.score_new = score_new |
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if self.current_search_dim == -1: |
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BaseOptimizer.evaluate(self, score_new) |
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else: |
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self.bayes_opt.evaluate(score_new) |
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""" |
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SimonBlanke /
Gradient-Free-Optimizers
