SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 4 |
| Total Lines | 39 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | # Author: Simon Blanke |
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| 2 | # Email: [email protected] |
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| 3 | # License: MIT License |
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| 4 | |||
| 5 | import numpy as np |
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| 6 | |||
| 7 | from gradient_free_optimizers import BayesianOptimizer |
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| 8 | from ._base_test import _base_test |
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| 9 | |||
| 10 | n_iter = 33 |
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| 11 | opt = BayesianOptimizer |
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| 12 | |||
| 13 | |||
| 14 | def test_skip_retrain(): |
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| 15 | for skip_retrain in ["many", "some", "few", "never"]: |
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| 16 | opt_para = {"skip_retrain": skip_retrain} |
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| 17 | _base_test(opt, n_iter, opt_para=opt_para) |
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| 18 | |||
| 19 | |||
| 20 | """ |
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| 21 | def test_warm_start_smbo(): |
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| 22 | gpr_X, gpr_y = [], [] |
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| 23 | for _ in range(10): |
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| 24 | pos = np.random.randint(0, high=9) |
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| 25 | pos = np.array([pos]) |
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| 26 | gpr_X.append(pos) |
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| 27 | gpr_y.append(get_score(pos)) |
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| 28 | |||
| 29 | for warm_start_smbo in [None, (gpr_X, gpr_y)]: |
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| 30 | opt_para = {"warm_start_smbo": warm_start_smbo} |
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| 31 | _base_test(opt, n_iter, opt_para=opt_para) |
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| 32 | """ |
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| 33 | |||
| 34 | |||
| 35 | def test_max_sample_size(): |
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| 36 | for max_sample_size in [10, 100, 10000, 10000000000]: |
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| 37 | opt_para = {"max_sample_size": max_sample_size} |
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| 38 | _base_test(opt, n_iter, opt_para=opt_para) |
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| 39 |
