SimonBlanke /
Hyperactive
| Total Complexity | 3 |
| Total Lines | 74 |
| Duplicated Lines | 54.05 % |
| 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 numpy as np |
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| 2 | from hyperactive import Hyperactive |
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| 3 | from hyperactive.data_tools import DataCollector |
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| 4 | |||
| 5 | |||
| 6 | View Code Duplication | def test_data_collector_0(): |
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Duplication
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by
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| 7 | def objective_function(opt): |
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| 8 | score = -opt["x1"] * opt["x1"] |
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| 9 | return score |
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| 10 | |||
| 11 | search_space = { |
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| 12 | "x1": np.arange(-100, 101, 1), |
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| 13 | } |
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| 14 | |||
| 15 | hyper = Hyperactive() |
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| 16 | hyper.add_search(objective_function, search_space, n_iter=20) |
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| 17 | hyper.run() |
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| 18 | |||
| 19 | search_data = hyper.results(objective_function) |
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| 20 | |||
| 21 | data_c = DataCollector("./search_data.csv") |
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| 22 | data_c.save(search_data) |
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| 23 | search_data_ = data_c.load() |
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| 24 | |||
| 25 | search_data.equals(search_data_) |
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| 26 | |||
| 27 | |||
| 28 | View Code Duplication | def test_data_collector_1(): |
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| 29 | def objective_function(opt): |
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| 30 | score = -opt["x1"] * opt["x1"] |
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| 31 | return score |
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| 32 | |||
| 33 | search_space = { |
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| 34 | "x1": np.arange(-100, 101, 1), |
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| 35 | } |
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| 36 | |||
| 37 | hyper = Hyperactive() |
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| 38 | hyper.add_search(objective_function, search_space, n_iter=20) |
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| 39 | hyper.run() |
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| 40 | |||
| 41 | search_data = hyper.results(objective_function) |
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| 42 | |||
| 43 | data_c = DataCollector("./search_data.csv") |
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| 44 | data_c.save(search_data, replace_existing=True) |
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| 45 | search_data_ = data_c.load() |
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| 46 | |||
| 47 | search_data.equals(search_data_) |
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| 48 | |||
| 49 | |||
| 50 | def test_data_collector_2(): |
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| 51 | data_c = DataCollector("./search_data.csv") |
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| 52 | |||
| 53 | def objective_function(opt): |
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| 54 | score = -opt["x1"] * opt["x1"] |
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| 55 | |||
| 56 | para_dict = { |
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| 57 | "x1": opt["x1"], |
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| 58 | "score": score, |
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| 59 | } |
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| 60 | |||
| 61 | data_c.append(para_dict) |
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| 62 | |||
| 63 | return score |
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| 64 | |||
| 65 | search_space = { |
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| 66 | "x1": np.arange(-100, 101, 1), |
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| 67 | } |
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| 68 | |||
| 69 | hyper = Hyperactive() |
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| 70 | hyper.add_search(objective_function, search_space, n_iter=20) |
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| 71 | hyper.run() |
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| 72 | |||
| 73 | search_data_ = data_c.load() |
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| 74 |
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