SimonBlanke /
Gradient-Free-Optimizers
| Conditions | 1 |
| Total Lines | 26 |
| Code Lines | 25 |
| Lines | 26 |
| Ratio | 100 % |
| Changes | 0 | ||
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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| 12 | def __init__( |
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| 13 | self, |
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| 14 | search_space: Dict[str, list], |
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| 15 | initialize: Dict[ |
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| 16 | Literal["grid", "vertices", "random", "warm_start"], int | List |
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| 17 | ] = {"grid": 4, "random": 2, "vertices": 4}, |
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| 18 | constraints: List[callable] = [], |
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| 19 | random_state: int = None, |
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| 20 | rand_rest_p: float = 0, |
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| 21 | nth_process: int = None, |
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| 22 | warm_start_smbo=None, |
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| 23 | max_sample_size: int = 10000000, |
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| 24 | sampling: Dict[Literal["random"], int] = {"random": 1000000}, |
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| 25 | replacement: bool = True, |
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| 26 | ): |
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| 27 | super().__init__( |
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| 28 | search_space=search_space, |
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| 29 | initialize=initialize, |
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| 30 | constraints=constraints, |
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| 31 | random_state=random_state, |
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| 32 | rand_rest_p=rand_rest_p, |
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| 33 | nth_process=nth_process, |
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| 34 | warm_start_smbo=warm_start_smbo, |
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| 35 | max_sample_size=max_sample_size, |
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| 36 | sampling=sampling, |
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| 37 | replacement=replacement, |
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| 38 | ) |
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| 39 |
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