hyperactive.opt.optuna._tpe_optimizer.TPEOptimizer.__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

TPEOptimizer.init() A

↳ Parent: hyperactive.opt.optuna._tpe_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

"""TPE (Tree-structured Parzen Estimator) optimizer."""
# copyright: hyperactive developers, MIT License (see LICENSE file)

from .._adapters._base_optuna_adapter import _BaseOptunaAdapter


class TPEOptimizer(_BaseOptunaAdapter):
    """Tree-structured Parzen Estimator 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.
    n_startup_trials : int, default=10
        Number of startup trials for TPE.
    n_ei_candidates : int, default=24
        Number of candidates for expected improvement.
    weights : callable, default=None
        Weight function for TPE.
    experiment : BaseExperiment, optional
        The experiment to optimize parameters for.
        Optional, can be passed later via ``set_params``.

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

    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from hyperactive.opt.optuna import TPEOptimizer
    >>> 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 = TPEOptimizer(
    ...     param_space=param_space, n_trials=50, experiment=sklearn_exp
    ... )
    >>> best_params = optimizer.run()
    """

    _tags = {
        "info:name": "Tree-structured Parzen Estimator",
        "info:local_vs_global": "global",
        "info:explore_vs_exploit": "exploit",
        "info:compute": "middle",
        "python_dependencies": ["optuna"],
    }

    def __init__(
        self,
        param_space=None,
        n_trials=100,
        initialize=None,
        random_state=None,
        early_stopping=None,
        max_score=None,
        n_startup_trials=10,
        n_ei_candidates=24,
        weights=None,
        experiment=None,
    ):
        self.n_startup_trials = n_startup_trials
        self.n_ei_candidates = n_ei_candidates
        self.weights = weights

        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 TPE optimizer.

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

        optimizer_kwargs = {
            "n_startup_trials": self.n_startup_trials,
            "n_ei_candidates": self.n_ei_candidates,
        }

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

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

        return optuna.samplers.TPESampler(**optimizer_kwargs)

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

        from hyperactive.experiment.integrations import SklearnCvExperiment

        # Test case 1: Basic TPE with standard parameters
        params = super().get_test_params(parameter_set)
        params[0].update(
            {
                "n_startup_trials": 5,
                "n_ei_candidates": 12,
            }
        )

        # Test case 2: Mixed parameter types with warm start
        X, y = load_wine(return_X_y=True)
        rf_exp = SklearnCvExperiment(
            estimator=RandomForestClassifier(random_state=42), X=X, y=y
        )

        mixed_param_space = {
            "n_estimators": (10, 100),  # Continuous integer
            "max_depth": [3, 5, 7, 10, None],  # Mixed discrete/None
            "criterion": ["gini", "entropy"],  # Categorical
            "min_samples_split": (2, 20),  # Continuous integer
            "bootstrap": [True, False],  # Boolean
        }

        # Warm start with known good configuration
        warm_start_points = [
            {
                "n_estimators": 50,
                "max_depth": 5,
                "criterion": "gini",
                "min_samples_split": 2,
                "bootstrap": True,
            }
        ]

        params.append(
            {
                "param_space": mixed_param_space,
                "n_trials": 20,
                "experiment": rf_exp,
                "n_startup_trials": 3,  # Fewer random trials before TPE
                "n_ei_candidates": 24,  # More EI candidates for better optimization
                "initialize": {"warm_start": warm_start_points},
            }
        )

        # Test case 3: High-dimensional continuous space (TPE strength)
        svm_exp = SklearnCvExperiment(estimator=SVC(), X=X, y=y)
        high_dim_space = {
            "C": (0.01, 100),
            "gamma": (1e-6, 1e2),
            "coef0": (0.0, 10.0),
            "degree": (2, 5),
            "tol": (1e-5, 1e-2),
        }

        params.append(
            {
                "param_space": high_dim_space,
                "n_trials": 25,
                "experiment": svm_exp,
                "n_startup_trials": 8,  # More startup for exploration
                "n_ei_candidates": 32,  # More candidates for complex space
            }
        )

        return params


1			"""TPE (Tree-structured Parzen Estimator) optimizer."""
2			# copyright: hyperactive developers, MIT License (see LICENSE file)
3
4			from .._adapters._base_optuna_adapter import _BaseOptunaAdapter
5
6
7			class TPEOptimizer(_BaseOptunaAdapter):
8			"""Tree-structured Parzen Estimator 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			n_startup_trials : int, default=10
30			Number of startup trials for TPE.
31			n_ei_candidates : int, default=24
32			Number of candidates for expected improvement.
33			weights : callable, default=None
34			Weight function for TPE.
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 TPEOptimizer with a scikit-learn experiment:
42
43			>>> from hyperactive.experiment.integrations import SklearnCvExperiment
44			>>> from hyperactive.opt.optuna import TPEOptimizer
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 = TPEOptimizer(
54			... param_space=param_space, n_trials=50, experiment=sklearn_exp
55			... )
56			>>> best_params = optimizer.run()
57			"""
58
59			_tags = {
60			"info:name": "Tree-structured Parzen Estimator",
61			"info:local_vs_global": "global",
62			"info:explore_vs_exploit": "exploit",
63			"info:compute": "middle",
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			n_startup_trials=10,
76			n_ei_candidates=24,
77			weights=None,
78			experiment=None,
79			):
80			self.n_startup_trials = n_startup_trials
81			self.n_ei_candidates = n_ei_candidates
82			self.weights = weights
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 TPE optimizer.
96
97			Returns
98			-------
99			optimizer
100			The Optuna TPEOptimizer instance
101			"""
102			import optuna
103
104			optimizer_kwargs = {
105			"n_startup_trials": self.n_startup_trials,
106			"n_ei_candidates": self.n_ei_candidates,
107			}
108
109			if self.weights is not None:
110			optimizer_kwargs["weights"] = self.weights
111
112			if self.random_state is not None:
113			optimizer_kwargs["seed"] = self.random_state
114
115			return optuna.samplers.TPESampler(**optimizer_kwargs)
116
117			@classmethod
118			def get_test_params(cls, parameter_set="default"):
119			"""Return testing parameter settings for the optimizer."""
120			from sklearn.datasets import load_wine
121			from sklearn.ensemble import RandomForestClassifier
122			from sklearn.svm import SVC
123
124			from hyperactive.experiment.integrations import SklearnCvExperiment
125
126			# Test case 1: Basic TPE with standard parameters
127			params = super().get_test_params(parameter_set)
128			params[0].update(
129			{
130			"n_startup_trials": 5,
131			"n_ei_candidates": 12,
132			}
133			)
134
135			# Test case 2: Mixed parameter types with warm start
136			X, y = load_wine(return_X_y=True)
137			rf_exp = SklearnCvExperiment(
138			estimator=RandomForestClassifier(random_state=42), X=X, y=y
139			)
140
141			mixed_param_space = {
142			"n_estimators": (10, 100), # Continuous integer
143			"max_depth": [3, 5, 7, 10, None], # Mixed discrete/None
144			"criterion": ["gini", "entropy"], # Categorical
145			"min_samples_split": (2, 20), # Continuous integer
146			"bootstrap": [True, False], # Boolean
147			}
148
149			# Warm start with known good configuration
150			warm_start_points = [
151			{
152			"n_estimators": 50,
153			"max_depth": 5,
154			"criterion": "gini",
155			"min_samples_split": 2,
156			"bootstrap": True,
157			}
158			]
159
160			params.append(
161			{
162			"param_space": mixed_param_space,
163			"n_trials": 20,
164			"experiment": rf_exp,
165			"n_startup_trials": 3, # Fewer random trials before TPE
166			"n_ei_candidates": 24, # More EI candidates for better optimization
167			"initialize": {"warm_start": warm_start_points},
168			}
169			)
170
171			# Test case 3: High-dimensional continuous space (TPE strength)
172			svm_exp = SklearnCvExperiment(estimator=SVC(), X=X, y=y)
173			high_dim_space = {
174			"C": (0.01, 100),
175			"gamma": (1e-6, 1e2),
176			"coef0": (0.0, 10.0),
177			"degree": (2, 5),
178			"tol": (1e-5, 1e-2),
179			}
180
181			params.append(
182			{
183			"param_space": high_dim_space,
184			"n_trials": 25,
185			"experiment": svm_exp,
186			"n_startup_trials": 8, # More startup for exploration
187			"n_ei_candidates": 32, # More candidates for complex space
188			}
189			)
190
191			return params
192

SimonBlanke / Hyperactive

Push — master ( c241e4...b050e9 )

TPEOptimizer.__init__() A

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