hyperactive.experiment.integrations.sklearn_cv.SklearnCvExperiment.get_test_params() - 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 11:41 UTC

SklearnCvExperiment.get_test_params() A

↳ Parent: hyperactive.experiment.integrations.sklearn_cv

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Total Lines	64
Code Lines	26

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eloc	26
dl	0
loc	64
rs	9.256
c	0
b	0
f	0
cc	1
nop	2

How to fix Long Method

"""Experiment adapter for sklearn cross-validation experiments."""

from sklearn import clone
from sklearn.metrics import check_scoring
from sklearn.model_selection import cross_validate
from sklearn.utils.validation import _num_samples

from hyperactive.base import BaseExperiment

class SklearnCvExperiment(BaseExperiment):
    """Experiment adapter for sklearn cross-validation experiments.

    This class is used to perform cross-validation experiments using a given
    sklearn estimator. It allows for hyperparameter tuning and evaluation of
    the model's performance using cross-validation.

    The score returned is the mean of the cross-validation scores,
    of applying cross-validation to ``estimator`` with the parameters given in
    ``score`` ``params``.

    The cross-validation performed is specified by the ``cv`` parameter,
    and the scoring metric is specified by the ``scoring`` parameter.
    The ``X`` and ``y`` parameters are the input data and target values,
    which are used in fit/predict cross-validation.

    Parameters
    ----------
    estimator : sklearn estimator
        The estimator to be used for the experiment.
    X : array-like, shape (n_samples, n_features)
            The input data for the model.
    y : array-like, shape (n_samples,) or (n_samples, n_outputs)
        The target values for the model.
    cv : int or cross-validation generator, default = KFold(n_splits=3, shuffle=True)
        The number of folds or cross-validation strategy to be used.
        If int, the cross-validation used is KFold(n_splits=cv, shuffle=True).
    scoring : callable or str, default = accuracy_score or mean_squared_error
        sklearn scoring function or metric to evaluate the model's performance.
        Default is determined by the type of estimator:
        ``accuracy_score`` for classifiers, and
        ``mean_squared_error`` for regressors, as per sklearn convention
        through the default ``score`` method of the estimator.

    Example
    -------
    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.svm import SVC
    >>> from sklearn.metrics import accuracy_score
    >>> from sklearn.model_selection import KFold
    >>>
    >>> X, y = load_iris(return_X_y=True)
    >>>
    >>> sklearn_exp = SklearnCvExperiment(
    ...    estimator=SVC(),
    ...     scoring=accuracy_score,
    ...     cv=KFold(n_splits=3, shuffle=True),
    ...     X=X,
    ...     y=y,
    ... )
    >>> params = {"C": 1.0, "kernel": "linear"}
    >>> score, add_info = sklearn_exp._score(params)
    """

    def __init__(self, estimator, X, y, scoring, cv):
        self.estimator = estimator
        self.X = X
        self.y = y
        self.scoring = scoring
        self.cv = cv

        super().__init__()

        if cv is None:
            from sklearn.model_selection import KFold

            self._cv = KFold(n_splits=3, shuffle=True)
        elif isinstance(cv, int):
            from sklearn.model_selection import KFold

            self._cv = KFold(n_splits=cv, shuffle=True)
        else:
            self._cv = cv

        self._scoring = check_scoring(estimator=estimator, scoring=scoring)

    def _paramnames(self):
        """Return the parameter names of the search.

        Returns
        -------
        list of str
            The parameter names of the search parameters.
        """
        return list(self.estimator.get_params().keys())

    def _score(self, params):
        """Score the parameters.

        Parameters
        ----------
        params : dict with string keys
            Parameters to score.

        Returns
        -------
        float
            The score of the parameters.
        dict
            Additional metadata about the search.
        """
        estimator = clone(self.estimator)
        estimator.set_params(**params)

        cv_results = cross_validate(
            estimator,
            self.X,
            self.y,
            cv=self._cv,
        )

        add_info_d = {
            "score_time": cv_results["score_time"],
            "fit_time": cv_results["fit_time"],
            "n_test_samples": _num_samples(self.X),
        }

        return cv_results["test_score"].mean(), add_info_d

    @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`
        """
        from sklearn.datasets import load_diabetes, load_iris
        from sklearn.svm import SVC, SVR
        from sklearn.metrics import accuracy_score, mean_absolute_error
        from sklearn.model_selection import KFold

        X, y = load_iris(return_X_y=True)
        params_classif = {
            "estimator": SVC(),
            "scoring": accuracy_score,
            "cv": KFold(n_splits=3, shuffle=True),
            "X": X,
            "y": y,
        }

        X, y = load_diabetes(return_X_y=True)
        params_regress = {
            "estimator": SVR(),
            "scoring": mean_absolute_error,
            "cv": 2,
            "X": X,
            "y": y,
        }

        X, y = load_diabetes(return_X_y=True)
        params_all_default = {
            "estimator": SVR(),
            "X": X,
            "y": y,
        }

        return [params_classif, params_regress, params_all_default]

    @classmethod
    def _get_score_params(self):
        """Return settings for the score function.

        Returns a list, the i-th element corresponds to self.get_test_params()[i].
        It should be a valid call for self.score.

        Returns
        -------
        list of dict
            The parameters to be used for scoring.
        """
        score_params_classif = {"C": 1.0, "kernel": "linear"}
        score_params_regress = {"C": 1.0, "kernel": "linear"}
        score_params_defaults = {"C": 1.0, "kernel": "linear"}
        return [score_params_classif, score_params_regress, score_params_defaults]


1			"""Experiment adapter for sklearn cross-validation experiments."""
2
3			from sklearn import clone
4			from sklearn.metrics import check_scoring
5			from sklearn.model_selection import cross_validate
6			from sklearn.utils.validation import _num_samples
7
8			from hyperactive.base import BaseExperiment
9
10			class SklearnCvExperiment(BaseExperiment):
11			"""Experiment adapter for sklearn cross-validation experiments.
12
13			This class is used to perform cross-validation experiments using a given
14			sklearn estimator. It allows for hyperparameter tuning and evaluation of
15			the model's performance using cross-validation.
16
17			The score returned is the mean of the cross-validation scores,
18			of applying cross-validation to ``estimator`` with the parameters given in
19			``score`` ``params``.
20
21			The cross-validation performed is specified by the ``cv`` parameter,
22			and the scoring metric is specified by the ``scoring`` parameter.
23			The ``X`` and ``y`` parameters are the input data and target values,
24			which are used in fit/predict cross-validation.
25
26			Parameters
27			----------
28			estimator : sklearn estimator
29			The estimator to be used for the experiment.
30			X : array-like, shape (n_samples, n_features)
31			The input data for the model.
32			y : array-like, shape (n_samples,) or (n_samples, n_outputs)
33			The target values for the model.
34			cv : int or cross-validation generator, default = KFold(n_splits=3, shuffle=True)
35			The number of folds or cross-validation strategy to be used.
36			If int, the cross-validation used is KFold(n_splits=cv, shuffle=True).
37			scoring : callable or str, default = accuracy_score or mean_squared_error
38			sklearn scoring function or metric to evaluate the model's performance.
39			Default is determined by the type of estimator:
40			``accuracy_score`` for classifiers, and
41			``mean_squared_error`` for regressors, as per sklearn convention
42			through the default ``score`` method of the estimator.
43
44			Example
45			-------
46			>>> from hyperactive.experiment.integrations import SklearnCvExperiment
47			>>> from sklearn.datasets import load_iris
48			>>> from sklearn.svm import SVC
49			>>> from sklearn.metrics import accuracy_score
50			>>> from sklearn.model_selection import KFold
51			>>>
52			>>> X, y = load_iris(return_X_y=True)
53			>>>
54			>>> sklearn_exp = SklearnCvExperiment(
55			... estimator=SVC(),
56			... scoring=accuracy_score,
57			... cv=KFold(n_splits=3, shuffle=True),
58			... X=X,
59			... y=y,
60			... )
61			>>> params = {"C": 1.0, "kernel": "linear"}
62			>>> score, add_info = sklearn_exp._score(params)
63			"""
64
65			def __init__(self, estimator, X, y, scoring, cv):
66			self.estimator = estimator
67			self.X = X
68			self.y = y
69			self.scoring = scoring
70			self.cv = cv
71
72			super().__init__()
73
74			if cv is None:
75			from sklearn.model_selection import KFold
76
77			self._cv = KFold(n_splits=3, shuffle=True)
78			elif isinstance(cv, int):
79			from sklearn.model_selection import KFold
80
81			self._cv = KFold(n_splits=cv, shuffle=True)
82			else:
83			self._cv = cv
84
85			self._scoring = check_scoring(estimator=estimator, scoring=scoring)
86
87			def _paramnames(self):
88			"""Return the parameter names of the search.
89
90			Returns
91			-------
92			list of str
93			The parameter names of the search parameters.
94			"""
95			return list(self.estimator.get_params().keys())
96
97			def _score(self, params):
98			"""Score the parameters.
99
100			Parameters
101			----------
102			params : dict with string keys
103			Parameters to score.
104
105			Returns
106			-------
107			float
108			The score of the parameters.
109			dict
110			Additional metadata about the search.
111			"""
112			estimator = clone(self.estimator)
113			estimator.set_params(**params)
114
115			cv_results = cross_validate(
116			estimator,
117			self.X,
118			self.y,
119			cv=self._cv,
120			)
121
122			add_info_d = {
123			"score_time": cv_results["score_time"],
124			"fit_time": cv_results["fit_time"],
125			"n_test_samples": _num_samples(self.X),
126			}
127
128			return cv_results["test_score"].mean(), add_info_d
129
130			@classmethod
131			def get_test_params(cls, parameter_set="default"):
132			"""Return testing parameter settings for the skbase object.
133
134			``get_test_params`` is a unified interface point to store
135			parameter settings for testing purposes. This function is also
136			used in ``create_test_instance`` and ``create_test_instances_and_names``
137			to construct test instances.
138
139			``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``.
140
141			Each ``dict`` is a parameter configuration for testing,
142			and can be used to construct an "interesting" test instance.
143			A call to ``cls(**params)`` should
144			be valid for all dictionaries ``params`` in the return of ``get_test_params``.
145
146			The ``get_test_params`` need not return fixed lists of dictionaries,
147			it can also return dynamic or stochastic parameter settings.
148
149			Parameters
150			----------
151			parameter_set : str, default="default"
152			Name of the set of test parameters to return, for use in tests. If no
153			special parameters are defined for a value, will return `"default"` set.
154
155			Returns
156			-------
157			params : dict or list of dict, default = {}
158			Parameters to create testing instances of the class
159			Each dict are parameters to construct an "interesting" test instance, i.e.,
160			`MyClass(params)` or `MyClass(params[i])` creates a valid test instance.
161			`create_test_instance` uses the first (or only) dictionary in `params`
162			"""
163			from sklearn.datasets import load_diabetes, load_iris
164			from sklearn.svm import SVC, SVR
165			from sklearn.metrics import accuracy_score, mean_absolute_error
166			from sklearn.model_selection import KFold
167
168			X, y = load_iris(return_X_y=True)
169			params_classif = {
170			"estimator": SVC(),
171			"scoring": accuracy_score,
172			"cv": KFold(n_splits=3, shuffle=True),
173			"X": X,
174			"y": y,
175			}
176
177			X, y = load_diabetes(return_X_y=True)
178			params_regress = {
179			"estimator": SVR(),
180			"scoring": mean_absolute_error,
181			"cv": 2,
182			"X": X,
183			"y": y,
184			}
185
186			X, y = load_diabetes(return_X_y=True)
187			params_all_default = {
188			"estimator": SVR(),
189			"X": X,
190			"y": y,
191			}
192
193			return [params_classif, params_regress, params_all_default]
194
195			@classmethod
196			def _get_score_params(self):
197			"""Return settings for the score function.
198
199			Returns a list, the i-th element corresponds to self.get_test_params()[i].
200			It should be a valid call for self.score.
201
202			Returns
203			-------
204			list of dict
205			The parameters to be used for scoring.
206			"""
207			score_params_classif = {"C": 1.0, "kernel": "linear"}
208			score_params_regress = {"C": 1.0, "kernel": "linear"}
209			score_params_defaults = {"C": 1.0, "kernel": "linear"}
210			return [score_params_classif, score_params_regress, score_params_defaults]
211

SimonBlanke / Hyperactive

Pull Request — master (#110)

SklearnCvExperiment.get_test_params() A

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