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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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indent = " " |
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def _print_times(eval_time, iter_time, n_iter): |
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opt_time = iter_time - eval_time |
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iterPerSec = n_iter / iter_time |
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print( |
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indent, |
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"Evaluation time :", |
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eval_time, |
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"sec", |
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indent, |
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"[{} %]".format(round(eval_time / iter_time * 100, 2)), |
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) |
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print( |
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indent, |
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"Optimization time :", |
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opt_time, |
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"sec", |
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indent, |
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"[{} %]".format(round(opt_time / iter_time * 100, 2)), |
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) |
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if iterPerSec >= 1: |
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print( |
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indent, |
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"Iteration time :", |
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iter_time, |
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"sec", |
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indent, |
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"[{} iter/sec]".format(round(iterPerSec, 2)), |
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) |
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else: |
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secPerIter = iter_time / n_iter |
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print( |
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indent, |
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"Iteration time :", |
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iter_time, |
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"sec", |
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indent, |
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"[{} sec/iter]".format(round(secPerIter, 2)), |
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) |
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print(" ") |
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def align_para_names(para_names): |
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str_lengths = [len(str_) for str_ in para_names] |
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max_length = max(str_lengths) |
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para_names_align = {} |
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for para_name, str_length in zip(para_names, str_lengths): |
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added_spaces = max_length - str_length |
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para_names_align[para_name] = " " * added_spaces |
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return para_names_align |
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def _print_results(objective_function, score_best, para_best, random_seed): |
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print("\nResults: '{}'".format(objective_function.__name__), " ") |
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if para_best is None: |
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print(indent, "Best score:", score_best, " ") |
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print(indent, "Best parameter:", para_best, " ") |
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else: |
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para_names = list(para_best.keys()) |
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para_names_align = align_para_names(para_names) |
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print(indent, "Best score:", score_best, " ") |
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print(indent, "Best parameter:") |
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for para_key in para_best.keys(): |
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added_spaces = para_names_align[para_key] |
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print( |
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indent, |
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indent, |
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"'{}'".format(para_key), |
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"{}:".format(added_spaces), |
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para_best[para_key], |
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" ", |
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) |
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print(" ") |
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print(indent, "Random seed:", random_seed, " ") |
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print(" ") |
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def print_info( |
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verbosity, |
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objective_function, |
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score_best, |
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para_best, |
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eval_times, |
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iter_times, |
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n_iter, |
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random_seed, |
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): |
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eval_time = np.array(eval_times).sum() |
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iter_time = np.array(iter_times).sum() |
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if "print_results" in verbosity: |
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_print_results(objective_function, score_best, para_best, random_seed) |
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if "print_times" in verbosity: |
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_print_times(eval_time, iter_time, n_iter) |
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SimonBlanke /
Gradient-Free-Optimizers
