SimonBlanke /
Hyperactive
| Conditions | 1 |
| Total Lines | 56 |
| Code Lines | 29 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | import numpy as np |
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| 7 | def test_issue_25(): |
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| 8 | # set a path to save the dataframe |
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| 9 | path = "./search_data.csv" |
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| 10 | search_space = { |
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| 11 | "n_neighbors": list(range(1, 50)), |
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| 12 | } |
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| 13 | |||
| 14 | # get para names from search space + the score |
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| 15 | para_names = list(search_space.keys()) + ["score"] |
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| 16 | |||
| 17 | # init empty pandas dataframe |
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| 18 | search_data = pd.DataFrame(columns=para_names) |
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| 19 | search_data.to_csv(path, index=False) |
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| 20 | |||
| 21 | def objective_function(para): |
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| 22 | # score = random.choice([1.2, 2.3, np.nan]) |
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| 23 | score = np.nan |
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| 24 | |||
| 25 | # you can access the entire dictionary from "para" |
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| 26 | parameter_dict = para.para_dict |
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| 27 | |||
| 28 | # save the score in the copy of the dictionary |
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| 29 | parameter_dict["score"] = score |
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| 30 | |||
| 31 | # append parameter dictionary to pandas dataframe |
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| 32 | search_data = pd.read_csv(path, na_values="nan") |
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| 33 | search_data_new = pd.DataFrame( |
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| 34 | parameter_dict, columns=para_names, index=[0] |
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| 35 | ) |
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| 36 | |||
| 37 | # search_data = search_data.append(search_data_new) |
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| 38 | search_data = pd.concat( |
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| 39 | [search_data, search_data_new], ignore_index=True |
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| 40 | ) |
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| 41 | |||
| 42 | search_data.to_csv(path, index=False, na_rep="nan") |
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| 43 | |||
| 44 | return score |
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| 45 | |||
| 46 | hyper0 = Hyperactive() |
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| 47 | hyper0.add_search(objective_function, search_space, n_iter=50) |
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| 48 | hyper0.run() |
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| 49 | |||
| 50 | search_data_0 = pd.read_csv(path, na_values="nan") |
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| 51 | """ |
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| 52 | the second run should be much faster than before, |
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| 53 | because Hyperactive already knows most parameters/scores |
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| 54 | """ |
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| 55 | hyper1 = Hyperactive() |
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| 56 | hyper1.add_search( |
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| 57 | objective_function, |
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| 58 | search_space, |
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| 59 | n_iter=50, |
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| 60 | memory_warm_start=search_data_0, |
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| 61 | ) |
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| 62 | hyper1.run() |
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| 63 |
