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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 ParameterGrid |
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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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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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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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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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Example |
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------- |
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Grid search applied to scikit-learn parameter tuning: |
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1. defining the experiment to optimize: |
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>>> from hyperactive.experiment.integrations import SklearnCvExperiment |
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>>> from sklearn.datasets import load_iris |
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>>> from sklearn.svm import SVC |
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>>> |
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>>> X, y = load_iris(return_X_y=True) |
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>>> |
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>>> sklearn_exp = SklearnCvExperiment( |
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... estimator=SVC(), |
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... X=X, |
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... y=y, |
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... ) |
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2. setting up the grid search optimizer: |
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>>> from hyperactive.opt import GridSearch |
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>>> param_grid = { |
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... "C": [0.01, 0.1, 1, 10], |
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... "gamma": [0.0001, 0.01, 0.1, 1, 10], |
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... } |
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>>> grid_search = GridSearch(param_grid, experiment=sklearn_exp) |
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3. running the grid search: |
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>>> best_params = grid_search.run() |
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Best parameters can also be accessed via the attributes: |
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>>> best_params = grid_search.best_params_ |
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""" |
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def __init__( |
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self, |
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param_grid=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_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_score_ = scores[best_index] |
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return best_params |
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@classmethod |
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def get_test_params(cls, parameter_set="default"): |
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"""Return testing parameter settings for the skbase object. |
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``get_test_params`` is a unified interface point to store |
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parameter settings for testing purposes. This function is also |
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used in ``create_test_instance`` and ``create_test_instances_and_names`` |
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to construct test instances. |
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``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``. |
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Each ``dict`` is a parameter configuration for testing, |
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and can be used to construct an "interesting" test instance. |
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A call to ``cls(**params)`` should |
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be valid for all dictionaries ``params`` in the return of ``get_test_params``. |
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The ``get_test_params`` need not return fixed lists of dictionaries, |
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it can also return dynamic or stochastic parameter settings. |
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Parameters |
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---------- |
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parameter_set : str, default="default" |
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Name of the set of test parameters to return, for use in tests. If no |
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special parameters are defined for a value, will return `"default"` set. |
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Returns |
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------- |
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params : dict or list of dict, default = {} |
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Parameters to create testing instances of the class |
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Each dict are parameters to construct an "interesting" test instance, i.e., |
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`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. |
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`create_test_instance` uses the first (or only) dictionary in `params` |
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""" |
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from hyperactive.experiment.integrations import SklearnCvExperiment |
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sklearn_exp = SklearnCvExperiment.create_test_instance() |
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param_grid = { |
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"C": [0.01, 0.1, 1, 10], |
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"gamma": [0.0001, 0.01, 0.1, 1, 10], |
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} |
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params_sklearn = { |
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"experiment": sklearn_exp, |
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"param_grid": param_grid, |
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} |
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from hyperactive.experiment.toy import Ackley |
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ackley_exp = Ackley.create_test_instance() |
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param_grid = { |
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"x0": np.linspace(-5, 5, 10), |
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"x1": np.linspace(-5, 5, 10), |
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} |
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params_ackley = { |
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"experiment": ackley_exp, |
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"param_grid": param_grid, |
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} |
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return [params_sklearn, params_ackley] |
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SimonBlanke /
Hyperactive
