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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 numpy as np |
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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 score_exceeded(score_best, max_score): |
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return max_score and score_best >= max_score |
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def no_change(score_new_list, early_stopping): |
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if "n_iter_no_change" not in early_stopping: |
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print( |
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"Warning n_iter_no_change-parameter must be set in order for early stopping to work" |
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) |
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return False |
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n_iter_no_change = early_stopping["n_iter_no_change"] |
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if len(score_new_list) <= n_iter_no_change: |
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return False |
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scores_np = np.array(score_new_list) |
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max_score = max(score_new_list) |
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max_index = np.argmax(scores_np) |
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length_pos = len(score_new_list) |
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diff = length_pos - max_index |
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if diff > n_iter_no_change: |
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return True |
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first_n = length_pos - n_iter_no_change |
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scores_first_n = score_new_list[:first_n] |
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max_first_n = max(scores_first_n) |
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if "tol_abs" in early_stopping and early_stopping["tol_abs"] is not None: |
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tol_abs = early_stopping["tol_abs"] |
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if abs(max_first_n - max_score) < tol_abs: |
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return True |
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if "tol_rel" in early_stopping and early_stopping["tol_rel"] is not None: |
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tol_rel = early_stopping["tol_rel"] |
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percent_imp = ((max_score - max_first_n) / abs(max_first_n)) * 100 |
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if percent_imp < tol_rel: |
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return True |
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class StopRun: |
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def __init__(self, start_time, max_time, max_score, early_stopping): |
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self.start_time = start_time |
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self.max_time = max_time |
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self.max_score = max_score |
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self.early_stopping = early_stopping |
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def update(self, score_best, score_new_list): |
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self.score_best = score_best |
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self.score_new_list = score_new_list |
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def check(self): |
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if self.max_time and time_exceeded(self.start_time, self.max_time): |
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return True |
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elif self.max_score and score_exceeded(self.score_best, self.max_score): |
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return True |
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elif self.early_stopping and no_change( |
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self.score_new_list, self.early_stopping |
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): |
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return True |
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SimonBlanke /
Gradient-Free-Optimizers
