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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from tqdm.auto import tqdm
class ProgressBarLVL0:
def __init__(self):
pass
def update(self, iter, score_new):
def close(self):
def _tqdm_dict(self, nth_process, n_iter, obj_func):
class ProgressBarLVL1:
def __init__(self, nth_process, n_iter, obj_func):
self.best_since_iter = 0
self.score_best = -np.inf
# tqdm.set_lock(tqdm.get_lock())
self._tqdm = tqdm(**self._tqdm_dict(nth_process, n_iter, obj_func))
self._tqdm.update(iter)
if score_new > self.score_best:
self.score_best = score_new
self.best_since_iter = self._tqdm.n
self._tqdm.set_postfix(
best_score=str(score_new), best_since_iter=self.best_since_iter
)
self._tqdm.close()
"""Generates the parameter dict for tqdm in the iteration-loop of each optimizer"""
return {
"total": n_iter,
"desc": "Process " + str(nth_process) + " -> " + obj_func.__name__,
"position": nth_process,
"leave": True,
}
SimonBlanke /
Gradient-Free-Optimizers
