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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 collections import OrderedDict |
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from ...local_opt import HillClimbingOptimizer |
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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(HillClimbingOptimizer): |
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name = "Powell's Method" |
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_name_ = "powells_method" |
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__name__ = "PowellsMethod" |
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optimizer_type = "global" |
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computationally_expensive = False |
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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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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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epsilon=0.03, |
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distribution="normal", |
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n_neighbours=3, |
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iters_p_dim=10, |
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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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epsilon=epsilon, |
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distribution=distribution, |
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n_neighbours=n_neighbours, |
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) |
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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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self.search_state = "iter" |
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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 = OrderedDict() |
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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] = np.array(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.hill_climb = HillClimbingOptimizer( |
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search_space=search_space_1D, |
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initialize={"random": 5}, |
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epsilon=self.epsilon, |
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distribution=self.distribution, |
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n_neighbours=self.n_neighbours, |
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) |
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@HillClimbingOptimizer.track_new_pos |
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@HillClimbingOptimizer.random_iteration |
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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.hill_climb.init_pos() |
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pos_new = self.hill_climb.conv.position2value(pos_new) |
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else: |
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pos_new = self.hill_climb.iterate() |
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pos_new = self.hill_climb.conv.position2value(pos_new) |
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pos_new = np.array(pos_new) |
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if self.conv.not_in_constraint(pos_new): |
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return pos_new |
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return self.move_climb( |
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pos_new, epsilon=self.epsilon, distribution=self.distribution |
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) |
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@HillClimbingOptimizer.track_new_score |
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def evaluate(self, score_new): |
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if self.current_search_dim == -1: |
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super(HillClimbingOptimizer, self).evaluate(score_new) |
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else: |
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self.hill_climb.evaluate(score_new) |
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super(HillClimbingOptimizer, self).evaluate(score_new) |
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SimonBlanke /
Gradient-Free-Optimizers
