| Total Complexity | 3 |
| Total Lines | 39 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | import time |
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| 2 | import numpy as np |
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| 3 | from hyperactive.optimizers import HillClimbingOptimizer |
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| 4 | from hyperactive.experiment import BaseExperiment |
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| 5 | from hyperactive.search_config import SearchConfig |
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| 6 | |||
| 7 | |||
| 8 | class Experiment(BaseExperiment): |
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| 9 | def objective_function(self, para): |
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| 10 | score = -para["x1"] * para["x1"] |
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| 11 | return score |
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| 12 | |||
| 13 | |||
| 14 | experiment = Experiment() |
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| 15 | |||
| 16 | search_config = SearchConfig( |
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| 17 | x1=list(np.arange(0, 100000, 1)), |
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| 18 | ) |
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| 19 | |||
| 20 | |||
| 21 | def test_max_time_0(): |
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| 22 | c_time1 = time.perf_counter() |
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| 23 | hyper = HillClimbingOptimizer() |
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| 24 | hyper.add_search(experiment, search_config, n_iter=1000000) |
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| 25 | hyper.run(max_time=0.1) |
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| 26 | diff_time1 = time.perf_counter() - c_time1 |
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| 27 | |||
| 28 | assert diff_time1 < 1 |
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| 29 | |||
| 30 | |||
| 31 | def test_max_time_1(): |
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| 32 | c_time1 = time.perf_counter() |
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| 33 | hyper = HillClimbingOptimizer() |
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| 34 | hyper.add_search(experiment, search_config, n_iter=1000000) |
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| 35 | hyper.run(max_time=1) |
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| 36 | diff_time1 = time.perf_counter() - c_time1 |
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| 37 | |||
| 38 | assert 0.3 < diff_time1 < 2 |
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| 39 |