hyperactive.opt.optuna._nsga_ii_optimizer.NSGAIIOptimizer.__init__() - Code Metrics - Inspection of "[ENH] `optuna` optimizer interface (#155)" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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created 2025-08-16 16:30 UTC

NSGAIIOptimizer.init() A

↳ Parent: hyperactive.opt.optuna._nsga_ii_optimizer

Complexity

Conditions

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Total Lines	25
Code Lines	23

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	23
dl	0
loc	25
rs	9.328
c	0
b	0
f	0
cc	1
nop	11

How to fix Many Parameters

"""NSGA-II multi-objective optimizer."""
# copyright: hyperactive developers, MIT License (see LICENSE file)

from .._adapters._base_optuna_adapter import _BaseOptunaAdapter


class NSGAIIOptimizer(_BaseOptunaAdapter):
    """NSGA-II multi-objective optimizer.

    Parameters
    ----------
    param_space : dict[str, tuple or list or optuna distributions]
        The search space to explore. Dictionary with parameter names
        as keys and either tuples/lists of (low, high) or
        optuna distribution objects as values.
    n_trials : int, default=100
        Number of optimization trials.
    initialize : dict[str, int], default=None
        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]}
    random_state : None, int, default=None
        If None, create a new random state. If int, create a new random state
        seeded with the value.
    early_stopping : int, default=None
        Number of trials after which to stop if no improvement.
    max_score : float, default=None
        Maximum score threshold. Stop optimization when reached.
    population_size : int, default=50
        Population size for NSGA-II.
    mutation_prob : float, default=0.1
        Mutation probability for NSGA-II.
    crossover_prob : float, default=0.9
        Crossover probability for NSGA-II.
    experiment : BaseExperiment, optional
        The experiment to optimize parameters for.
        Optional, can be passed later via ``set_params``.

    Examples
    --------
    Basic usage of NSGAIIOptimizer with a scikit-learn experiment:

    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from hyperactive.opt.optuna import NSGAIIOptimizer
    >>> 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)
    >>> param_space = {
    ...     "C": (0.01, 10),
    ...     "gamma": (0.0001, 10),
    ... }
    >>> optimizer = NSGAIIOptimizer(
    ...     param_space=param_space, n_trials=50, experiment=sklearn_exp
    ... )
    >>> best_params = optimizer.run()
    """

    _tags = {
        "info:name": "NSGA-II Optimizer",
        "info:local_vs_global": "global",
        "info:explore_vs_exploit": "mixed",
        "info:compute": "high",
        "python_dependencies": ["optuna"],
    }

    def __init__(
        self,
        param_space=None,
        n_trials=100,
        initialize=None,
        random_state=None,
        early_stopping=None,
        max_score=None,
        population_size=50,
        mutation_prob=0.1,
        crossover_prob=0.9,
        experiment=None,
    ):
        self.population_size = population_size
        self.mutation_prob = mutation_prob
        self.crossover_prob = crossover_prob

        super().__init__(
            param_space=param_space,
            n_trials=n_trials,
            initialize=initialize,
            random_state=random_state,
            early_stopping=early_stopping,
            max_score=max_score,
            experiment=experiment,
        )

    def _get_optimizer(self):
        """Get the NSGA-II optimizer.

        Returns
        -------
        optimizer
            The Optuna NSGAIIOptimizer instance
        """
        import optuna

        optimizer_kwargs = {
            "population_size": self.population_size,
            "mutation_prob": self.mutation_prob,
            "crossover_prob": self.crossover_prob,
        }

        if self.random_state is not None:
            optimizer_kwargs["seed"] = self.random_state

        return optuna.samplers.NSGAIISampler(**optimizer_kwargs)

    @classmethod

    def get_test_params(cls, parameter_set="default"):
        """Return testing parameter settings for the optimizer."""
        from sklearn.datasets import load_iris
        from sklearn.ensemble import RandomForestClassifier

        from hyperactive.experiment.integrations import SklearnCvExperiment

        # Test case 1: Basic single-objective (inherits from base)
        params = super().get_test_params(parameter_set)
        params[0].update(
            {
                "population_size": 20,
                "mutation_prob": 0.2,
                "crossover_prob": 0.8,
            }
        )

        # Test case 2: Multi-objective with mixed parameter types
        X, y = load_iris(return_X_y=True)
        rf_exp = SklearnCvExperiment(
            estimator=RandomForestClassifier(random_state=42), X=X, y=y
        )

        mixed_param_space = {
            "n_estimators": (10, 50),  # Continuous integer
            "max_depth": [3, 5, 7, None],  # Mixed discrete/None
            "criterion": ["gini", "entropy"],  # Categorical
            "min_samples_split": (2, 10),  # Continuous integer
            "bootstrap": [True, False],  # Boolean categorical
        }

        params.append(
            {
                "param_space": mixed_param_space,
                "n_trials": 15,  # Smaller for faster testing
                "experiment": rf_exp,
                "population_size": 8,  # Smaller population for testing
                "mutation_prob": 0.1,
                "crossover_prob": 0.9,
            }
        )

        return params


1		"""NSGA-II multi-objective optimizer."""
2		# copyright: hyperactive developers, MIT License (see LICENSE file)
3
4		from .._adapters._base_optuna_adapter import _BaseOptunaAdapter
5
6
7		class NSGAIIOptimizer(_BaseOptunaAdapter):
8		"""NSGA-II multi-objective optimizer.
9
10		Parameters
11		----------
12		param_space : dict[str, tuple or list or optuna distributions]
13		The search space to explore. Dictionary with parameter names
14		as keys and either tuples/lists of (low, high) or
15		optuna distribution objects as values.
16		n_trials : int, default=100
17		Number of optimization trials.
18		initialize : dict[str, int], default=None
19		The method to generate initial positions. A dictionary with
20		the following key literals and the corresponding value type:
21		{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
22		random_state : None, int, default=None
23		If None, create a new random state. If int, create a new random state
24		seeded with the value.
25		early_stopping : int, default=None
26		Number of trials after which to stop if no improvement.
27		max_score : float, default=None
28		Maximum score threshold. Stop optimization when reached.
29		population_size : int, default=50
30		Population size for NSGA-II.
31		mutation_prob : float, default=0.1
32		Mutation probability for NSGA-II.
33		crossover_prob : float, default=0.9
34		Crossover probability for NSGA-II.
35		experiment : BaseExperiment, optional
36		The experiment to optimize parameters for.
37		Optional, can be passed later via ``set_params``.
38
39		Examples
40		--------
41		Basic usage of NSGAIIOptimizer with a scikit-learn experiment:
42
43		>>> from hyperactive.experiment.integrations import SklearnCvExperiment
44		>>> from hyperactive.opt.optuna import NSGAIIOptimizer
45		>>> from sklearn.datasets import load_iris
46		>>> from sklearn.svm import SVC
47		>>> X, y = load_iris(return_X_y=True)
48		>>> sklearn_exp = SklearnCvExperiment(estimator=SVC(), X=X, y=y)
49		>>> param_space = {
50		... "C": (0.01, 10),
51		... "gamma": (0.0001, 10),
52		... }
53		>>> optimizer = NSGAIIOptimizer(
54		... param_space=param_space, n_trials=50, experiment=sklearn_exp
55		... )
56		>>> best_params = optimizer.run()
57		"""
58
59		_tags = {
60		"info:name": "NSGA-II Optimizer",
61		"info:local_vs_global": "global",
62		"info:explore_vs_exploit": "mixed",
63		"info:compute": "high",
64		"python_dependencies": ["optuna"],
65		}
66
67		def __init__(
68		self,
69		param_space=None,
70		n_trials=100,
71		initialize=None,
72		random_state=None,
73		early_stopping=None,
74		max_score=None,
75		population_size=50,
76		mutation_prob=0.1,
77		crossover_prob=0.9,
78		experiment=None,
79		):
80		self.population_size = population_size
81		self.mutation_prob = mutation_prob
82		self.crossover_prob = crossover_prob
83
84		super().__init__(
85		param_space=param_space,
86		n_trials=n_trials,
87		initialize=initialize,
88		random_state=random_state,
89		early_stopping=early_stopping,
90		max_score=max_score,
91		experiment=experiment,
92		)
93
94		def _get_optimizer(self):
95		"""Get the NSGA-II optimizer.
96
97		Returns
98		-------
99		optimizer
100		The Optuna NSGAIIOptimizer instance
101		"""
102		import optuna
103
104		optimizer_kwargs = {
105		"population_size": self.population_size,
106		"mutation_prob": self.mutation_prob,
107		"crossover_prob": self.crossover_prob,
108		}
109
110		if self.random_state is not None:
111		optimizer_kwargs["seed"] = self.random_state
112
113		return optuna.samplers.NSGAIISampler(**optimizer_kwargs)
114
115	View Code Duplication	@classmethod
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116		def get_test_params(cls, parameter_set="default"):
117		"""Return testing parameter settings for the optimizer."""
118		from sklearn.datasets import load_iris
119		from sklearn.ensemble import RandomForestClassifier
120
121		from hyperactive.experiment.integrations import SklearnCvExperiment
122
123		# Test case 1: Basic single-objective (inherits from base)
124		params = super().get_test_params(parameter_set)
125		params[0].update(
126		{
127		"population_size": 20,
128		"mutation_prob": 0.2,
129		"crossover_prob": 0.8,
130		}
131		)
132
133		# Test case 2: Multi-objective with mixed parameter types
134		X, y = load_iris(return_X_y=True)
135		rf_exp = SklearnCvExperiment(
136		estimator=RandomForestClassifier(random_state=42), X=X, y=y
137		)
138
139		mixed_param_space = {
140		"n_estimators": (10, 50), # Continuous integer
141		"max_depth": [3, 5, 7, None], # Mixed discrete/None
142		"criterion": ["gini", "entropy"], # Categorical
143		"min_samples_split": (2, 10), # Continuous integer
144		"bootstrap": [True, False], # Boolean categorical
145		}
146
147		params.append(
148		{
149		"param_space": mixed_param_space,
150		"n_trials": 15, # Smaller for faster testing
151		"experiment": rf_exp,
152		"population_size": 8, # Smaller population for testing
153		"mutation_prob": 0.1,
154		"crossover_prob": 0.9,
155		}
156		)
157
158		return params
159

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NSGAIIOptimizer.__init__() A

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