hyperactive.opt.gfo.hillclimbing.HillClimbing.get_search_config() - Code Metrics - Inspection of "V5 API rework - experimental" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#110)

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created 2025-05-05 16:23 UTC

HillClimbing.get_search_config() A

↳ Parent: hyperactive.opt.gfo.hillclimbing

Complexity

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Total Lines	12
Code Lines	5

Duplication

Lines	0
Ratio	0 %

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Metric	Value
eloc	5
dl	0
loc	12
rs	10
c	0
b	0
f	0
cc	1
nop	1

"""Hill climbing optimizer from gfo."""

from gradient_free_optimizers import HillClimbingOptimizer
from hyperactive.base import BaseOptimizer
from skbase.utils.stdout_mute import StdoutMute


class HillClimbing(BaseOptimizer):
    """Hill climbing optimizer.

    Parameters
    ----------
    experiment : BaseExperiment, optional
        The experiment to optimize parameters for.
        Optional, can be passed later in ``add_search``.
    random_state : None, int, default=None
        If None, create a new random state. If int, create a new random state
        seeded with the value.
    rand_rest_p : float, default=0.1
        The probability of a random iteration during the the search process.
    epsilon : float, default=0.01
        The step-size for the climbing.
    distribution : str, default="normal"
        The type of distribution to sample from.
    n_neighbours : int, default=10
        The number of neighbours to sample and evaluate before moving to the best
        of those neighbours.
    search_space : dict[str, list]
        The search space to explore. A dictionary with parameter
        names as keys and a numpy array as values.
        Optional, can be passed later in ``add_search``.
    initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4}
        The method to generate initial positions. A dictionary with
        the following key literals and the corresponding value type:
        {"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
    constraints : list[callable], default=[]
        A list of constraints, where each constraint is a callable.
        The callable returns `True` or `False` dependend on the input parameters.
    n_iter : int, default=100
        The number of iterations to run the optimizer.
    verbose : bool, default=False
        If True, print the progress of the optimization process.

    Examples
    --------
    Hill climbing applied to scikit-learn parameter tuning:

    1. defining the experiment to optimize:
    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.svm import SVC
    >>>
    >>> X, y = load_iris(return_X_y=True)
    >>>
    >>> sklearn_exp = SklearnCvExperiment(
    ...     estimator=SVC(),
    ...     X=X,
    ...     y=y,
    ... )

    2. setting up the hill climbing optimizer:
    >>> from hyperactive.opt import HillClimbing
    >>> import numpy as np
    >>> 
    >>> hillclimbing_config = {
    ...     "search_space": {
    ...         "C": np.array([0.01, 0.1, 1, 10]),
    ...         "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
    ...     },
    ...     "n_iter": 100,
    ... }
    >>> hillclimbing = HillClimbing(sklearn_exp, **hillclimbing_config)

    3. running the hill climbing search:
    >>> best_params = hillclimbing.run()

    Best parameters can also be accessed via the attributes:
    >>> best_params = hillclimbing.best_params_
    """

    _tags = {
        "python_dependencies": ["gradient-free-optimizers>=1.5.0"],
    }

    def __init__(
        self,
        experiment=None,
        random_state=None,
        rand_rest_p=0.1,
        epsilon=0.01,
        distribution="normal",
        n_neighbours=10,
        search_space=None,
        initialize=None,
        constraints=None,
        n_iter=100,
        verbose=False,
    ):
        self.random_state = random_state
        self.rand_rest_p = rand_rest_p
        self.epsilon = epsilon
        self.distribution = distribution
        self.n_neighbours = n_neighbours
        self.search_space = search_space
        self.initialize = initialize
        self.constraints = constraints
        self.n_iter = n_iter
        self.experiment = experiment
        self.verbose = verbose

        super().__init__()

        if initialize is None:
            self._initialize = {"grid": 4, "random": 2, "vertices": 4}
        else:
            self._initialize = initialize

    def get_search_config(self):
        """Get the search configuration.

        Returns
        -------
        dict with str keys
            The search configuration dictionary.
        """
        search_config = super().get_search_config()
        search_config["initialize"] = self._initialize
        del search_config["verbose"]
        return search_config

    def _run(self, experiment, **search_config):
        """Run the optimization search process."""
        n_iter = search_config.pop("n_iter", 100)
        max_time = search_config.pop("max_time", None)

        hcopt = HillClimbingOptimizer(**search_config)

        with StdoutMute(active=not self.verbose):
            hcopt.search(
                objective_function=experiment.score,
                n_iter=n_iter,
                max_time=max_time,
            )
        self.best_params_ = hcopt.best_para

        return self.best_params_

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Return testing parameter settings for the skbase object.

        ``get_test_params`` is a unified interface point to store
        parameter settings for testing purposes. This function is also
        used in ``create_test_instance`` and ``create_test_instances_and_names``
        to construct test instances.

        ``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``.

        Each ``dict`` is a parameter configuration for testing,
        and can be used to construct an "interesting" test instance.
        A call to ``cls(**params)`` should
        be valid for all dictionaries ``params`` in the return of ``get_test_params``.

        The ``get_test_params`` need not return fixed lists of dictionaries,
        it can also return dynamic or stochastic parameter settings.

        Parameters
        ----------
        parameter_set : str, default="default"
            Name of the set of test parameters to return, for use in tests. If no
            special parameters are defined for a value, will return `"default"` set.

        Returns
        -------
        params : dict or list of dict, default = {}
            Parameters to create testing instances of the class
            Each dict are parameters to construct an "interesting" test instance, i.e.,
            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
            `create_test_instance` uses the first (or only) dictionary in `params`
        """
        import numpy as np
        from hyperactive.experiment.integrations import SklearnCvExperiment

        sklearn_exp = SklearnCvExperiment.create_test_instance()
        params_sklearn = {
            "experiment": sklearn_exp,
            "search_space": {
                "C": np.array([0.01, 0.1, 1, 10]),
                "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
            },
            "n_iter": 100,
        }

        from hyperactive.experiment.toy import Ackley

        ackley_exp = Ackley.create_test_instance()
        params_ackley = {
            "experiment": ackley_exp,
            "search_space": {
                "x0": np.linspace(-5, 5, 10),
                "x1": np.linspace(-5, 5, 10),
            },
            "n_iter": 100,
        }
        
        return [params_sklearn, params_ackley]


1			"""Hill climbing optimizer from gfo."""
2
3			from gradient_free_optimizers import HillClimbingOptimizer
4			from hyperactive.base import BaseOptimizer
5			from skbase.utils.stdout_mute import StdoutMute
6
7
8			class HillClimbing(BaseOptimizer):
9			"""Hill climbing optimizer.
10
11			Parameters
12			----------
13			experiment : BaseExperiment, optional
14			The experiment to optimize parameters for.
15			Optional, can be passed later in ``add_search``.
16			random_state : None, int, default=None
17			If None, create a new random state. If int, create a new random state
18			seeded with the value.
19			rand_rest_p : float, default=0.1
20			The probability of a random iteration during the the search process.
21			epsilon : float, default=0.01
22			The step-size for the climbing.
23			distribution : str, default="normal"
24			The type of distribution to sample from.
25			n_neighbours : int, default=10
26			The number of neighbours to sample and evaluate before moving to the best
27			of those neighbours.
28			search_space : dict[str, list]
29			The search space to explore. A dictionary with parameter
30			names as keys and a numpy array as values.
31			Optional, can be passed later in ``add_search``.
32			initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4}
33			The method to generate initial positions. A dictionary with
34			the following key literals and the corresponding value type:
35			{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
36			constraints : list[callable], default=[]
37			A list of constraints, where each constraint is a callable.
38			The callable returns `True` or `False` dependend on the input parameters.
39			n_iter : int, default=100
40			The number of iterations to run the optimizer.
41			verbose : bool, default=False
42			If True, print the progress of the optimization process.
43
44			Examples
45			--------
46			Hill climbing applied to scikit-learn parameter tuning:
47
48			1. defining the experiment to optimize:
49			>>> from hyperactive.experiment.integrations import SklearnCvExperiment
50			>>> from sklearn.datasets import load_iris
51			>>> from sklearn.svm import SVC
52			>>>
53			>>> X, y = load_iris(return_X_y=True)
54			>>>
55			>>> sklearn_exp = SklearnCvExperiment(
56			... estimator=SVC(),
57			... X=X,
58			... y=y,
59			... )
60
61			2. setting up the hill climbing optimizer:
62			>>> from hyperactive.opt import HillClimbing
63			>>> import numpy as np
64			>>>
65			>>> hillclimbing_config = {
66			... "search_space": {
67			... "C": np.array([0.01, 0.1, 1, 10]),
68			... "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
69			... },
70			... "n_iter": 100,
71			... }
72			>>> hillclimbing = HillClimbing(sklearn_exp, **hillclimbing_config)
73
74			3. running the hill climbing search:
75			>>> best_params = hillclimbing.run()
76
77			Best parameters can also be accessed via the attributes:
78			>>> best_params = hillclimbing.best_params_
79			"""
80
81			_tags = {
82			"python_dependencies": ["gradient-free-optimizers>=1.5.0"],
83			}
84
85			def __init__(
86			self,
87			experiment=None,
88			random_state=None,
89			rand_rest_p=0.1,
90			epsilon=0.01,
91			distribution="normal",
92			n_neighbours=10,
93			search_space=None,
94			initialize=None,
95			constraints=None,
96			n_iter=100,
97			verbose=False,
98			):
99			self.random_state = random_state
100			self.rand_rest_p = rand_rest_p
101			self.epsilon = epsilon
102			self.distribution = distribution
103			self.n_neighbours = n_neighbours
104			self.search_space = search_space
105			self.initialize = initialize
106			self.constraints = constraints
107			self.n_iter = n_iter
108			self.experiment = experiment
109			self.verbose = verbose
110
111			super().__init__()
112
113			if initialize is None:
114			self._initialize = {"grid": 4, "random": 2, "vertices": 4}
115			else:
116			self._initialize = initialize
117
118			def get_search_config(self):
119			"""Get the search configuration.
120
121			Returns
122			-------
123			dict with str keys
124			The search configuration dictionary.
125			"""
126			search_config = super().get_search_config()
127			search_config["initialize"] = self._initialize
128			del search_config["verbose"]
129			return search_config
130
131			def _run(self, experiment, **search_config):
132			"""Run the optimization search process."""
133			n_iter = search_config.pop("n_iter", 100)
134			max_time = search_config.pop("max_time", None)
135
136			hcopt = HillClimbingOptimizer(**search_config)
137
138			with StdoutMute(active=not self.verbose):
139			hcopt.search(
140			objective_function=experiment.score,
141			n_iter=n_iter,
142			max_time=max_time,
143			)
144			self.best_params_ = hcopt.best_para
145
146			return self.best_params_
147
148			@classmethod
149			def get_test_params(cls, parameter_set="default"):
150			"""Return testing parameter settings for the skbase object.
151
152			``get_test_params`` is a unified interface point to store
153			parameter settings for testing purposes. This function is also
154			used in ``create_test_instance`` and ``create_test_instances_and_names``
155			to construct test instances.
156
157			``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``.
158
159			Each ``dict`` is a parameter configuration for testing,
160			and can be used to construct an "interesting" test instance.
161			A call to ``cls(**params)`` should
162			be valid for all dictionaries ``params`` in the return of ``get_test_params``.
163
164			The ``get_test_params`` need not return fixed lists of dictionaries,
165			it can also return dynamic or stochastic parameter settings.
166
167			Parameters
168			----------
169			parameter_set : str, default="default"
170			Name of the set of test parameters to return, for use in tests. If no
171			special parameters are defined for a value, will return `"default"` set.
172
173			Returns
174			-------
175			params : dict or list of dict, default = {}
176			Parameters to create testing instances of the class
177			Each dict are parameters to construct an "interesting" test instance, i.e.,
178			`MyClass(params)` or `MyClass(params[i])` creates a valid test instance.
179			`create_test_instance` uses the first (or only) dictionary in `params`
180			"""
181			import numpy as np
182			from hyperactive.experiment.integrations import SklearnCvExperiment
183
184			sklearn_exp = SklearnCvExperiment.create_test_instance()
185			params_sklearn = {
186			"experiment": sklearn_exp,
187			"search_space": {
188			"C": np.array([0.01, 0.1, 1, 10]),
189			"gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
190			},
191			"n_iter": 100,
192			}
193
194			from hyperactive.experiment.toy import Ackley
195
196			ackley_exp = Ackley.create_test_instance()
197			params_ackley = {
198			"experiment": ackley_exp,
199			"search_space": {
200			"x0": np.linspace(-5, 5, 10),
201			"x1": np.linspace(-5, 5, 10),
202			},
203			"n_iter": 100,
204			}
205
206			return [params_sklearn, params_ackley]
207

SimonBlanke / Hyperactive

Pull Request — master (#110)

HillClimbing.get_search_config() A

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