SimonBlanke /
Hyperactive
| Conditions | 2 |
| Total Lines | 41 |
| Code Lines | 36 |
| Lines | 0 |
| Ratio | 0 % |
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Methods with many parameters are not only hard to understand, but their parameters also often become inconsistent when you need more, or different data.
There are several approaches to avoid long parameter lists:
| 1 | # Author: Simon Blanke |
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| 6 | def _process_( |
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| 7 | nth_process, |
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| 8 | objective_function, |
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| 9 | search_space, |
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| 10 | optimizer, |
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| 11 | n_iter, |
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| 12 | memory, |
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| 13 | memory_warm_start, |
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| 14 | max_time, |
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| 15 | max_score, |
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| 16 | random_state, |
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| 17 | verbosity, |
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| 18 | **kwargs |
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| 19 | ): |
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| 20 | if "progress_bar" in verbosity: |
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| 21 | verbosity_gfo = ["progress_bar"] |
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| 22 | else: |
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| 23 | verbosity_gfo = [] |
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| 24 | |||
| 25 | optimizer.search( |
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| 26 | objective_function=objective_function, |
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| 27 | n_iter=n_iter, |
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| 28 | max_time=max_time, |
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| 29 | max_score=max_score, |
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| 30 | memory=memory, |
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| 31 | memory_warm_start=memory_warm_start, |
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| 32 | verbosity=verbosity_gfo, |
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| 33 | random_state=random_state, |
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| 34 | nth_process=nth_process, |
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| 35 | ) |
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| 36 | |||
| 37 | return { |
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| 38 | "nth_process": nth_process, |
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| 39 | "best_para": optimizer.best_para, |
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| 40 | "best_score": optimizer.best_score, |
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| 41 | "best_iter": optimizer.p_bar._best_since_iter, |
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| 42 | "eval_times": optimizer.eval_time, |
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| 43 | "iter_times": optimizer.iter_time, |
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| 44 | "positions": optimizer.positions, |
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| 45 | "results": optimizer.results, |
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| 46 | "memory_values_df": optimizer.memory_values_df, |
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| 47 | } |
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| 48 |
