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1
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import pytest |
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2
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from tqdm import tqdm |
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3
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import numpy as np |
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4
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5
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from ._parametrize import optimizers |
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6
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7
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8
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n_iter_para = ("n_iter", [(10), (20), (30)]) |
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9
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10
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11
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@pytest.mark.parametrize(*n_iter_para) |
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12
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@pytest.mark.parametrize(*optimizers) |
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13
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def test_search_tracker(Optimizer, n_iter): |
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14
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def objective_function(para): |
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15
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score = -para["x1"] * para["x1"] |
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16
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return score |
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17
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18
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search_space = {"x1": np.arange(-10, 11, 1)} |
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19
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initialize = {"vertices": 1} |
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20
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21
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opt = Optimizer(search_space, initialize=initialize) |
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22
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opt.search( |
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23
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objective_function, |
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24
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n_iter=n_iter, |
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25
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memory=False, |
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26
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verbosity=False, |
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27
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) |
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28
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29
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n_new_positions = 0 |
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30
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n_new_scores = 0 |
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31
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32
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n_current_positions = 0 |
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33
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n_current_scores = 0 |
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34
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35
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n_best_positions = 0 |
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36
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n_best_scores = 0 |
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37
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38
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optimizers = opt.optimizers |
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39
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for optimizer in optimizers: |
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40
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n_new_positions = n_new_positions + len(optimizer.pos_new_list) |
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41
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n_new_scores = n_new_scores + len(optimizer.score_new_list) |
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42
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43
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n_current_positions = n_current_positions + len(optimizer.pos_current_list) |
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44
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n_current_scores = n_current_scores + len(optimizer.score_current_list) |
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45
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46
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n_best_positions = n_best_positions + len(optimizer.pos_best_list) |
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47
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n_best_scores = n_best_scores + len(optimizer.score_best_list) |
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48
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49
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assert n_new_positions == n_iter |
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50
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assert n_new_scores == n_iter |
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51
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52
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assert n_current_positions == n_current_scores |
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53
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assert n_current_positions <= n_new_positions |
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54
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55
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assert n_best_positions == n_best_scores |
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56
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assert n_best_positions <= n_new_positions |
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57
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SimonBlanke /
Gradient-Free-Optimizers
