SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 13 |
| Total Lines | 59 |
| Duplicated Lines | 16.95 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
| 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 | |||
| 9 | n_iter = 100 |
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| 10 | |||
| 11 | |||
| 12 | def get_score(pos_new): |
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| 13 | return -(pos_new[0] * pos_new[0]) |
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| 14 | |||
| 15 | |||
| 16 | space_dim = np.array([10]) |
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| 17 | init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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| 18 | |||
| 19 | |||
| 20 | View Code Duplication | def _base_test(opt): |
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Duplication
introduced
by
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| 21 | for nth_init in range(len(init_positions)): |
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| 22 | pos_new = opt.init_pos(nth_init) |
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| 23 | score_new = get_score(pos_new) |
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| 24 | opt.evaluate(score_new) |
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| 25 | |||
| 26 | for nth_iter in range(len(init_positions), n_iter): |
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| 27 | pos_new = opt.iterate(nth_iter) |
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| 28 | score_new = get_score(pos_new) |
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| 29 | opt.evaluate(score_new) |
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| 30 | |||
| 31 | |||
| 32 | def _test_BayesianOptimizer(opt_para): |
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| 33 | opt = BayesianOptimizer(init_positions, space_dim, opt_para=opt_para) |
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| 34 | _base_test(opt) |
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| 36 | |||
| 37 | def test_skip_retrain(): |
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| 38 | for skip_retrain in ["many", "some", "few", "never"]: |
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| 39 | opt_para = {"skip_retrain": skip_retrain} |
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| 40 | _test_BayesianOptimizer(opt_para) |
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| 41 | |||
| 42 | |||
| 43 | def test_start_up_evals(): |
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| 44 | for start_up_evals in [0, 1, 100]: |
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| 45 | opt_para = {"start_up_evals": start_up_evals} |
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| 46 | _test_BayesianOptimizer(opt_para) |
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| 47 | |||
| 48 | |||
| 49 | def test_warm_start_smbo(): |
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| 50 | for warm_start_smbo in [True, False]: |
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| 51 | opt_para = {"warm_start_smbo": warm_start_smbo} |
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| 52 | _test_BayesianOptimizer(opt_para) |
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| 54 | |||
| 55 | def test_max_sample_size(): |
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| 56 | for max_sample_size in [10, 100, 10000, 10000000000]: |
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| 57 | opt_para = {"max_sample_size": max_sample_size} |
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| 58 | _test_BayesianOptimizer(opt_para) |
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| 59 |
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