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"""Grid search optimizer.""" |
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# copyright: hyperactive developers, MIT License (see LICENSE file) |
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from collections.abc import Sequence |
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import numpy as np |
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from sklearn.model_selection import ParameterSampler |
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from hyperactive.base import BaseOptimizer |
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class RandomSearchSk(BaseOptimizer): |
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"""Random search optimizer leveraging sklearn's ``ParameterSampler``. |
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Parameters |
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---------- |
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param_distributions : dict[str, list | scipy.stats.rv_frozen] |
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Search space specification. Discrete lists are sampled uniformly; |
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scipy distribution objects are sampled via their ``rvs`` method. |
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n_iter : int, default=10 |
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Number of parameter sets to evaluate. |
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random_state : int | np.random.RandomState | None, default=None |
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Controls the pseudo-random generator for reproducibility. |
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error_score : float, default=np.nan |
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Score assigned when the experiment raises an exception. |
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experiment : BaseExperiment, optional |
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Callable returning a scalar score when invoked with keyword |
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arguments matching a parameter set. |
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Attributes |
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---------- |
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best_params_ : dict[str, Any] |
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Hyper-parameter configuration with the best (lowest) score. |
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best_score_ : float |
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Score achieved by ``best_params_``. |
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best_index_ : int |
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Index of ``best_params_`` in the sampled sequence. |
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""" |
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def __init__( |
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self, |
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param_distributions=None, |
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n_iter=10, |
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random_state=None, |
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error_score=np.nan, |
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experiment=None, |
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): |
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self.experiment = experiment |
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self.param_distributions = param_distributions |
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self.n_iter = n_iter |
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self.random_state = random_state |
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self.error_score = error_score |
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super().__init__() |
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@staticmethod |
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def _is_distribution(obj) -> bool: |
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"""Return True if *obj* looks like a scipy frozen distribution.""" |
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return callable(getattr(obj, "rvs", None)) |
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def _check_param_distributions(self, param_distributions): |
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"""Validate ``param_distributions`` similar to sklearn ≤1.0.x.""" |
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if hasattr(param_distributions, "items"): |
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param_distributions = [param_distributions] |
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for p in param_distributions: |
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for name, v in p.items(): |
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if self._is_distribution(v): |
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# Assume scipy frozen distribution - nothing to check |
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continue |
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if isinstance(v, np.ndarray) and v.ndim > 1: |
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raise ValueError("Parameter array should be one-dimensional.") |
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if isinstance(v, str) or not isinstance(v, (np.ndarray, Sequence)): |
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raise ValueError( |
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f"Parameter distribution for ({name}) must be a list, numpy " |
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f"array, or scipy.stats ``rv_frozen``, but got ({type(v)})." |
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" Single values need to be wrapped in a sequence." |
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) |
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if len(v) == 0: |
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raise ValueError( |
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f"Parameter values for ({name}) need to be a " |
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"non-empty sequence." |
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) |
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def _solve( |
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self, |
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experiment, |
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param_distributions, |
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n_iter, |
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random_state, |
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error_score, |
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): |
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"""Sample ``n_iter`` points and return the best parameter set.""" |
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self._check_param_distributions(param_distributions) |
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sampler = ParameterSampler( |
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param_distributions=param_distributions, |
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n_iter=n_iter, |
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random_state=random_state, |
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) |
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candidate_params = list(sampler) |
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scores: list[float] = [] |
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for candidate_param in candidate_params: |
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try: |
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score = experiment(**candidate_param) |
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except Exception: # noqa: B904 |
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score = error_score |
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scores.append(score) |
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best_index = int(np.argmin(scores)) # lower-is-better convention |
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best_params = candidate_params[best_index] |
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# public attributes for external consumers |
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self.best_index_ = best_index |
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self.best_score_ = float(scores[best_index]) |
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self.best_params_ = best_params |
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return best_params |
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@classmethod |
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def get_test_params(cls, parameter_set: str = "default"): |
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"""Provide deterministic toy configurations for unit tests.""" |
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from hyperactive.experiment.integrations import SklearnCvExperiment |
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from hyperactive.experiment.toy import Ackley |
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# 1) ML example (Iris + SVC) |
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sklearn_exp = SklearnCvExperiment.create_test_instance() |
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param_dist_1 = { |
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"C": [0.01, 0.1, 1, 10], |
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"gamma": np.logspace(-4, 1, 6), |
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} |
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params_sklearn = { |
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"experiment": sklearn_exp, |
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"param_distributions": param_dist_1, |
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"n_iter": 5, |
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"random_state": 42, |
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} |
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# 2) continuous optimisation example (Ackley) |
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ackley_exp = Ackley.create_test_instance() |
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param_dist_2 = { |
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"x0": np.linspace(-5, 5, 50), |
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"x1": np.linspace(-5, 5, 50), |
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} |
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params_ackley = { |
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"experiment": ackley_exp, |
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"param_distributions": param_dist_2, |
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"n_iter": 20, |
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"random_state": 0, |
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} |
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return [params_sklearn, params_ackley] |
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SimonBlanke /
Hyperactive
