| 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 |