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"""Hill climbing optimizer from gfo.""" |
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from gradient_free_optimizers import HillClimbingOptimizer |
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from hyperactive.base import BaseOptimizer |
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class HillClimbing(BaseOptimizer): |
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"""Hill climbing optimizer. |
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Parameters |
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---------- |
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experiment : BaseExperiment, optional |
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The experiment to optimize parameters for. |
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Optional, can be passed later in ``add_search``. |
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random_state : None, int, default=None |
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If None, create a new random state. If int, create a new random state |
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seeded with the value. |
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rand_rest_p : float, default=0.1 |
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The probability of a random iteration during the the search process. |
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epsilon : float, default=0.01 |
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The step-size for the climbing. |
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distribution : str, default="uniform" |
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The type of distribution to sample from. |
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n_neighbours : int, default=10 |
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The number of neighbours to sample and evaluate before moving to the best |
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of those neighbours. |
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search_space : dict[str, list] |
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The search space to explore. A dictionary with parameter |
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names as keys and a numpy array as values. |
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Optional, can be passed later in ``add_search``. |
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initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4} |
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The method to generate initial positions. A dictionary with |
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the following key literals and the corresponding value type: |
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{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]} |
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constraints : list[callable], default=[] |
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A list of constraints, where each constraint is a callable. |
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The callable returns `True` or `False` dependend on the input parameters. |
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n_iter : int, default=100 |
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The number of iterations to run the optimizer. |
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""" |
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def __init__( |
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self, |
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experiment=None, |
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random_state=None, |
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rand_rest_p=0.1, |
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epsilon=0.01, |
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distribution="uniform", |
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n_neighbours=10, |
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search_space=None, |
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initialize=None, |
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constraints=None, |
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n_iter=100, |
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): |
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self.random_state = random_state |
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self.rand_rest_p = rand_rest_p |
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self.epsilon = epsilon |
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self.distribution = distribution |
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self.n_neighbours = n_neighbours |
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self.search_space = search_space |
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self.initialize = initialize |
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self.constraints = constraints |
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self.n_iter = n_iter |
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self.experiment = experiment |
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super().__init__() |
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if initialize is None: |
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self._initialize = {"grid": 4, "random": 2, "vertices": 4} |
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else: |
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self._initialize = initialize |
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def add_search(self, experiment, **search_config): |
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"""Add a new optimization search process with specified parameters. |
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Parameters |
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--------- |
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experiment : BaseExperiment |
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The experiment to optimize parameters for. |
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search_space : dict[str, list] |
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The search space to explore. A dictionary with parameter |
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names as keys and a numpy array as values. |
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initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4} |
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The method to generate initial positions. A dictionary with |
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the following key literals and the corresponding value type: |
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{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]} |
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constraints : list[callable], default=[] |
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A list of constraints, where each constraint is a callable. |
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The callable returns `True` or `False` dependend on the input parameters. |
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n_iter : int, default=100 |
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The number of iterations to run the optimizer. |
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""" |
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super().add_search(experiment, **search_config) |
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def get_search_config(self): |
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"""Get the search configuration. |
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Returns |
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------- |
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dict with str keys |
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The search configuration dictionary. |
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""" |
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search_config = super().get_search_config() |
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search_config["initialize"] = self._initialize |
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def _run(self, experiment, **search_config): |
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"""Run the optimization search process. |
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Parameters |
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---------- |
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max_time : float |
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The maximum time used for the optimization process. |
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""" |
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n_iter = search_config.pop("n_iter", 100) |
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max_time = search_config.pop("max_time", None) |
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hcopt = HillClimbingOptimizer(**search_config) |
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hcopt.search( |
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objective_function=experiment, |
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n_iter=n_iter, |
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max_time=max_time, |
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) |
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self.best_params_ = hcopt.best_params() |
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return self.best_params_ |
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SimonBlanke /
Hyperactive
