SimonBlanke /
Hyperactive
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| Total Lines | 57 |
| Code Lines | 18 |
| Lines | 0 |
| Ratio | 0 % |
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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 | # Author: Simon Blanke |
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| 25 | def __init__(self, *args, **kwargs): |
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| 26 | |||
| 27 | """ |
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| 28 | |||
| 29 | Parameters |
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| 30 | ---------- |
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| 31 | |||
| 32 | search_config: dict |
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| 33 | A dictionary providing the model and hyperparameter search space for the |
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| 34 | optimization process. |
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| 35 | n_iter: int |
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| 36 | The number of iterations the optimizer performs. |
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| 37 | metric: string, optional (default: "accuracy") |
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| 38 | The metric the model is evaluated by. |
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| 39 | n_jobs: int, optional (default: 1) |
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| 40 | The number of searches to run in parallel. |
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| 41 | cv: int, optional (default: 3) |
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| 42 | The number of folds for the cross validation. |
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| 43 | verbosity: int, optional (default: 1) |
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| 44 | Verbosity level. 1 prints out warm_start points and their scores. |
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| 45 | random_state: int, optional (default: None) |
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| 46 | Sets the random seed. |
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| 47 | warm_start: dict, optional (default: False) |
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| 48 | Dictionary that definies a start point for the optimizer. |
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| 49 | memory: bool, optional (default: True) |
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| 50 | A memory, that saves the evaluation during the optimization to save time when |
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| 51 | optimizer returns to position. |
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| 52 | scatter_init: int, optional (default: False) |
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| 53 | Defines the number n of random positions that should be evaluated with 1/n the |
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| 54 | training data, to find a better initial position. |
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| 55 | |||
| 56 | Returns |
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| 57 | ------- |
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| 58 | None |
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| 59 | |||
| 60 | """ |
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| 61 | |||
| 62 | optimizer_dict = { |
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| 63 | "HillClimbing": HillClimbingOptimizer, |
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| 64 | "StochasticHillClimbing": StochasticHillClimbingOptimizer, |
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| 65 | "TabuSearch": TabuOptimizer, |
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| 66 | "RandomSearch": RandomSearchOptimizer, |
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| 67 | "RandomRestartHillClimbing": RandomRestartHillClimbingOptimizer, |
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| 68 | "RandomAnnealing": RandomAnnealingOptimizer, |
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| 69 | "SimulatedAnnealing": SimulatedAnnealingOptimizer, |
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| 70 | "StochasticTunneling": StochasticTunnelingOptimizer, |
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| 71 | "ParallelTempering": ParallelTemperingOptimizer, |
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| 72 | "ParticleSwarm": ParticleSwarmOptimizer, |
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| 73 | "EvolutionStrategy": EvolutionStrategyOptimizer, |
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| 74 | "Bayesian": BayesianOptimizer, |
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| 75 | } |
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| 76 | |||
| 77 | _config_ = Config(*args, **kwargs) |
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| 78 | _arg_ = Arguments(**_config_.opt_para) |
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| 79 | |||
| 80 | optimizer_class = optimizer_dict[_config_.optimizer] |
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| 81 | self._optimizer_ = optimizer_class(_config_, _arg_) |
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| 82 | |||
| 98 |
