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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 time |
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import random |
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import numpy as np |
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from tqdm import tqdm |
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from .init_positions import init_grid_search, init_random_search |
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from .progress_bar import ProgressBarLVL0, ProgressBarLVL1 |
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p_bar_dict = { |
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0: ProgressBarLVL0, |
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1: ProgressBarLVL1, |
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} |
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def time_exceeded(start_time, max_time): |
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run_time = time.time() - start_time |
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return max_time and run_time > max_time |
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def set_random_seed(nth_process, random_state): |
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"""Sets the random seed separately for each thread (to avoid getting the same results in each thread)""" |
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if random_state is None: |
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random_state = np.random.randint(0, high=2 ** 32 - 2) |
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random.seed(random_state + nth_process) |
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np.random.seed(random_state + nth_process) |
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class Search: |
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def _values2positions(self, values): |
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init_pos_conv_list = [] |
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values_np = np.array(values) |
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for n, space_dim in enumerate(self.search_space): |
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pos_1d = values_np[:, n] |
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init_pos_conv = np.where(space_dim == pos_1d)[0] |
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init_pos_conv_list.append(init_pos_conv) |
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return init_pos_conv_list |
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def _positions2values(self, positions): |
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pos_converted = [] |
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positions_np = np.array(positions) |
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for n, space_dim in enumerate(self.search_space): |
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pos_1d = positions_np[:, n] |
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pos_conv = np.take(space_dim, pos_1d, axis=0) |
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pos_converted.append(pos_conv) |
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return list(np.array(pos_converted).T) |
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def _init_values(self, init_values): |
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init_positions_list = [] |
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if "random" in init_values: |
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positions = init_random_search(self.space_dim, init_values["random"]) |
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init_positions_list.append(positions) |
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if "grid" in init_values: |
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positions = init_grid_search(self.space_dim, init_values["grid"]) |
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init_positions_list.append(positions) |
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if "warm_start" in init_values: |
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positions = self._values2positions(init_values["warm_start"]) |
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init_positions_list.append(positions) |
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return [item for sublist in init_positions_list for item in sublist] |
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def _position2value(self, position): |
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value = [] |
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for n, space_dim in enumerate(self.search_space): |
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value.append(space_dim[position[n]]) |
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return value |
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def _score_mem(self, pos): |
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pos_tuple = tuple(pos) |
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if pos_tuple in self.memory_dict: |
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return self.memory_dict[pos_tuple] |
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else: |
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score = self.objective_function(pos) |
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self.memory_dict[pos_tuple] = score |
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return score |
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def _init_memory(self, memory): |
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if memory == False: |
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self._score = self.objective_function |
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elif memory == True: |
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self._score = self._score_mem |
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self.memory_dict = {} |
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elif isinstance(memory, dict): |
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self._score = self._score_mem |
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values_list = memory["values"] |
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scores = memory["scores"] |
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value_tuple_list = list(map(tuple, values_list)) |
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self.memory_dict = dict(zip(value_tuple_list, scores)) |
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def search( |
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self, |
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objective_function, |
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n_iter, |
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initialize={"grid": 7, "random": 3,}, |
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max_time=None, |
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memory=True, |
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verbosity=1, |
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random_state=None, |
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nth_process=0, |
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): |
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self.objective_function = objective_function |
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self._init_memory(memory) |
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set_random_seed(nth_process, random_state) |
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start_time = time.time() |
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self.p_bar = p_bar_dict[verbosity](nth_process, n_iter, objective_function) |
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init_values = self._init_values(initialize) |
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# loop to initialize N positions |
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for init_position in init_values: |
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start_time_iter = time.time() |
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self.init_pos(init_position) |
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start_time_eval = time.time() |
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score_new = self._score(init_position) |
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self.p_bar.update(1, score_new) |
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self.eval_times.append(time.time() - start_time_eval) |
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self.evaluate(score_new) |
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self.iter_times.append(time.time() - start_time_iter) |
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# loop to do the iterations |
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for nth_iter in range(len(init_values), n_iter): |
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start_time_iter = time.time() |
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pos_new = self.iterate() |
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value_new = self._position2value(pos_new) |
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start_time_eval = time.time() |
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score_new = self._score(value_new) |
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self.p_bar.update(1, score_new) |
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self.eval_times.append(time.time() - start_time_eval) |
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self.evaluate(score_new) |
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self.iter_times.append(time.time() - start_time_iter) |
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if time_exceeded(start_time, max_time): |
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break |
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self.p_bar.close() |
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SimonBlanke /
Gradient-Free-Optimizers
