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"""Grid search optimizer.""" |
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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 ParameterGrid, ParameterSampler, check_cv |
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from hyperactive.base import BaseOptimizer |
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class GridSearch(BaseOptimizer): |
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"""Grid search 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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error_score : float, default=np.nan |
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The score to assign if an error occurs during the evaluation of a parameter set. |
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param_grid : 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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""" |
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def __init__( |
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self, |
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experiment=None, |
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error_score=np.nan, |
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param_grid=None, |
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): |
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self.experiment = experiment |
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self.param_grid = param_grid |
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self.error_score = error_score |
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super().__init__() |
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def _check_param_grid(self, param_grid): |
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"""_check_param_grid from sklearn 1.0.2, before it was removed.""" |
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if hasattr(param_grid, "items"): |
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param_grid = [param_grid] |
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for p in param_grid: |
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for name, v in p.items(): |
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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 grid for parameter ({name}) needs to" |
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f" be a list or numpy array, but got ({type(v)})." |
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" Single values need to be wrapped in a list" |
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" with one element." |
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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 parameter ({name}) need " |
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"to be a non-empty sequence." |
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) |
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def _run(self, experiment, param_grid, error_score): |
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"""Run the optimization search process.""" |
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self._check_param_grid(param_grid) |
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candidate_params = list(ParameterGrid(param_grid)) |
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scores = [] |
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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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# Catch all exceptions and assign error_score |
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score = error_score |
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scores.append(score) |
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best_index = np.argmin(scores) |
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best_params = candidate_params[best_index] |
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self.best_index_ = best_index |
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self.best_params_ = best_params |
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self.best_score_ = scores[best_index] |
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return best_params |
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SimonBlanke /
Hyperactive
