| Total Complexity | 4 |
| Total Lines | 66 |
| Duplicated Lines | 84.85 % |
| 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 | import pytest |
||
| 2 | from tqdm import tqdm |
||
| 3 | import numpy as np |
||
| 4 | |||
| 5 | from ._parametrize import optimizers_noSBOM, optimizers_SBOM |
||
| 6 | |||
| 7 | |||
| 8 | View Code Duplication | @pytest.mark.parametrize(*optimizers_noSBOM) |
|
|
|
|||
| 9 | def test_convex_convergence_noSBOM(Optimizer): |
||
| 10 | def objective_function(para): |
||
| 11 | score = -para["x1"] * para["x1"] |
||
| 12 | return score |
||
| 13 | |||
| 14 | search_space = {"x1": np.arange(-33, 33, 1)} |
||
| 15 | initialize = {"vertices": 2} |
||
| 16 | |||
| 17 | n_opts = 33 |
||
| 18 | |||
| 19 | scores = [] |
||
| 20 | for rnd_st in tqdm(range(n_opts)): |
||
| 21 | opt = Optimizer(search_space) |
||
| 22 | opt.search( |
||
| 23 | objective_function, |
||
| 24 | n_iter=50, |
||
| 25 | random_state=rnd_st, |
||
| 26 | memory=False, |
||
| 27 | verbosity={"print_results": False, "progress_bar": False}, |
||
| 28 | initialize=initialize, |
||
| 29 | ) |
||
| 30 | |||
| 31 | scores.append(opt.best_score) |
||
| 32 | score_mean = np.array(scores).mean() |
||
| 33 | print("scores", scores) |
||
| 34 | |||
| 35 | assert -500 < score_mean |
||
| 36 | |||
| 37 | |||
| 38 | View Code Duplication | @pytest.mark.parametrize(*optimizers_SBOM) |
|
| 39 | def test_convex_convergence_SBOM(Optimizer): |
||
| 40 | def objective_function(para): |
||
| 41 | score = -para["x1"] * para["x1"] |
||
| 42 | return score |
||
| 43 | |||
| 44 | search_space = {"x1": np.arange(-33, 33, 1)} |
||
| 45 | initialize = {"vertices": 2} |
||
| 46 | |||
| 47 | n_opts = 10 |
||
| 48 | |||
| 49 | scores = [] |
||
| 50 | for rnd_st in tqdm(range(n_opts)): |
||
| 51 | opt = Optimizer(search_space) |
||
| 52 | opt.search( |
||
| 53 | objective_function, |
||
| 54 | n_iter=30, |
||
| 55 | random_state=rnd_st, |
||
| 56 | memory=False, |
||
| 57 | verbosity={"print_results": False, "progress_bar": False}, |
||
| 58 | initialize=initialize, |
||
| 59 | ) |
||
| 60 | |||
| 61 | scores.append(opt.best_score) |
||
| 62 | score_mean = np.array(scores).mean() |
||
| 63 | print("scores", scores) |
||
| 64 | |||
| 65 | assert -500 < score_mean |
||
| 66 | |||
| 67 |