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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 tqdm import tqdm |
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class ProgressBarBase: |
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def __init__(self, nth_process, n_iter, objective_function): |
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self.best_since_iter = 0 |
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self.score_best = -np.inf |
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self.values_best = None |
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self.objective_function = objective_function |
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def _new2best(self, score_new, values_new): |
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if score_new > self.score_best: |
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self.score_best = score_new |
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self.values_best = values_new |
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def _print_results(self, print_results): |
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if print_results: |
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print("\nResults: '{}'".format(self.objective_function.__name__), " ") |
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print(" Best values", np.array(self.values_best), " ") |
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print(" Best score", self.score_best, " ") |
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class ProgressBarLVL0(ProgressBarBase): |
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def __init__(self, nth_process, n_iter, objective_function): |
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super().__init__(nth_process, n_iter, objective_function) |
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def update(self, score_new, values_new): |
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self._new2best(score_new, values_new) |
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def close(self, print_results): |
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self._print_results(print_results) |
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class ProgressBarLVL1(ProgressBarBase): |
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def __init__(self, nth_process, n_iter, objective_function): |
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self.best_since_iter = 0 |
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self.score_best = -np.inf |
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self.values_best = None |
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self._tqdm = tqdm(**self._tqdm_dict(nth_process, n_iter, objective_function)) |
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def update(self, score_new, values_new): |
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self._tqdm.update() |
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self._new2best(score_new, values_new) |
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if score_new > self.score_best: |
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self.best_since_iter = self._tqdm.n - 1 |
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self._tqdm.set_postfix( |
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best_score=str(score_new), best_iter=str(self.best_since_iter) |
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) |
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self._tqdm.refresh() |
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def close(self, print_results): |
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self._tqdm.close() |
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self._print_results(print_results) |
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def _tqdm_dict(self, nth_process, n_iter, objective_function): |
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"""Generates the parameter dict for tqdm in the iteration-loop of each optimizer""" |
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self.objective_function = objective_function |
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if nth_process is None: |
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process_str = "" |
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else: |
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process_str = "Process " + str(nth_process) + " -> " |
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return { |
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"total": n_iter, |
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"desc": process_str + objective_function.__name__, |
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"position": nth_process, |
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"leave": True, |
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"smoothing": 1.0, |
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} |
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SimonBlanke /
Gradient-Free-Optimizers
