Total Complexity | 1 |
Total Lines | 58 |
Duplicated Lines | 0 % |
Changes | 0 |
1 | import time |
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2 | import pytest |
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3 | import numpy as np |
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4 | |||
5 | |||
6 | from hyperactive import Hyperactive |
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7 | from hyperactive.optimizers.strategies import CustomOptimizationStrategy |
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8 | from hyperactive.optimizers import GridSearchOptimizer |
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9 | |||
10 | from ._parametrize import optimizers_smbo |
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11 | |||
12 | |||
13 | @pytest.mark.parametrize(*optimizers_smbo) |
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14 | def test_memory_Warm_start_smbo_0(Optimizer_smbo): |
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15 | def objective_function(opt): |
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16 | time.sleep(0.01) |
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17 | score = -(opt["x1"] * opt["x1"]) |
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18 | return score |
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19 | |||
20 | search_space = { |
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21 | "x1": list(np.arange(0, 100, 1)), |
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22 | } |
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23 | |||
24 | optimizer1 = GridSearchOptimizer() |
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25 | optimizer2 = Optimizer_smbo() |
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26 | |||
27 | opt_strat = CustomOptimizationStrategy() |
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28 | |||
29 | duration_1 = 0.8 |
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30 | duration_2 = 0.2 |
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31 | |||
32 | opt_strat.add_optimizer(optimizer1, duration=duration_1) |
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33 | opt_strat.add_optimizer(optimizer2, duration=duration_2) |
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34 | |||
35 | n_iter = 20 |
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36 | |||
37 | hyper = Hyperactive() |
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38 | hyper.add_search( |
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39 | objective_function, |
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40 | search_space, |
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41 | optimizer=opt_strat, |
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42 | n_iter=n_iter, |
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43 | memory=True, |
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44 | ) |
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45 | hyper.run() |
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46 | |||
47 | search_data = hyper.search_data(objective_function) |
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48 | |||
49 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
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50 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
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51 | |||
52 | assert len(search_data) == n_iter |
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53 | |||
54 | assert len(optimizer1.search_data) == int(n_iter * duration_1) |
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55 | assert len(optimizer2.search_data) == int(n_iter * duration_2) |
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56 | |||
57 | assert optimizer1.best_score <= optimizer2.best_score |
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58 |