hyperactive.experiment.integrations.sklearn_cv.SklearnCvExperiment.__init__() - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

SklearnCvExperiment.init() B
last analyzed 2025-06-23 18:24 UTC

↳ Parent: hyperactive.experiment.integrations.sklearn_cv

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Total Lines	34
Code Lines	23

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eloc	23
dl	0
loc	34
rs	8.3946
c	0
b	0
f	0
cc	6
nop	6

"""Experiment adapter for sklearn cross-validation experiments."""
# copyright: hyperactive developers, MIT License (see LICENSE file)

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)

    For default choices of ``scoring`` and ``cv``:
    >>> sklearn_exp = SklearnCvExperiment(
    ...     estimator=SVC(),
    ...     X=X,
    ...     y=y,
    ... )
    >>> params = {"C": 1.0, "kernel": "linear"}
    >>> score, add_info = sklearn_exp.score(params)

    Quick call without metadata return or dictionary:
    >>> score = sklearn_exp(C=1.0, kernel="linear")
    """

    def __init__(self, estimator, X, y, scoring=None, cv=None):
        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

        # check if scoring is a scorer by checking for "estimator" in signature
        if scoring is None:
            self._scoring = check_scoring(self.estimator)
        # check using inspect.signature for "estimator" in signature
        elif callable(scoring):
            from inspect import signature

            if "estimator" in signature(scoring).parameters:
                self._scoring = scoring
            else:
                from sklearn.metrics import make_scorer

                self._scoring = make_scorer(scoring)
        self.scorer_ = self._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,
            scoring=self._scoring,
            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 testing the score function. Used in tests only.

        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			# copyright: hyperactive developers, MIT License (see LICENSE file)
3
4			from sklearn import clone
5			from sklearn.metrics import check_scoring
6			from sklearn.model_selection import cross_validate
7			from sklearn.utils.validation import _num_samples
8
9			from hyperactive.base import BaseExperiment
10
11			class SklearnCvExperiment(BaseExperiment):
12			"""Experiment adapter for sklearn cross-validation experiments.
13
14			This class is used to perform cross-validation experiments using a given
15			sklearn estimator. It allows for hyperparameter tuning and evaluation of
16			the model's performance using cross-validation.
17
18			The score returned is the mean of the cross-validation scores,
19			of applying cross-validation to ``estimator`` with the parameters given in
20			``score`` ``params``.
21
22			The cross-validation performed is specified by the ``cv`` parameter,
23			and the scoring metric is specified by the ``scoring`` parameter.
24			The ``X`` and ``y`` parameters are the input data and target values,
25			which are used in fit/predict cross-validation.
26
27			Parameters
28			----------
29			estimator : sklearn estimator
30			The estimator to be used for the experiment.
31			X : array-like, shape (n_samples, n_features)
32			The input data for the model.
33			y : array-like, shape (n_samples,) or (n_samples, n_outputs)
34			The target values for the model.
35			cv : int or cross-validation generator, default = KFold(n_splits=3, shuffle=True)
36			The number of folds or cross-validation strategy to be used.
37			If int, the cross-validation used is KFold(n_splits=cv, shuffle=True).
38			scoring : callable or str, default = accuracy_score or mean_squared_error
39			sklearn scoring function or metric to evaluate the model's performance.
40			Default is determined by the type of estimator:
41			``accuracy_score`` for classifiers, and
42			``mean_squared_error`` for regressors, as per sklearn convention
43			through the default ``score`` method of the estimator.
44
45			Example
46			-------
47			>>> from hyperactive.experiment.integrations import SklearnCvExperiment
48			>>> from sklearn.datasets import load_iris
49			>>> from sklearn.svm import SVC
50			>>> from sklearn.metrics import accuracy_score
51			>>> from sklearn.model_selection import KFold
52			>>>
53			>>> X, y = load_iris(return_X_y=True)
54			>>>
55			>>> sklearn_exp = SklearnCvExperiment(
56			... estimator=SVC(),
57			... scoring=accuracy_score,
58			... cv=KFold(n_splits=3, shuffle=True),
59			... X=X,
60			... y=y,
61			... )
62			>>> params = {"C": 1.0, "kernel": "linear"}
63			>>> score, add_info = sklearn_exp.score(params)
64
65			For default choices of ``scoring`` and ``cv``:
66			>>> sklearn_exp = SklearnCvExperiment(
67			... estimator=SVC(),
68			... X=X,
69			... y=y,
70			... )
71			>>> params = {"C": 1.0, "kernel": "linear"}
72			>>> score, add_info = sklearn_exp.score(params)
73
74			Quick call without metadata return or dictionary:
75			>>> score = sklearn_exp(C=1.0, kernel="linear")
76			"""
77
78			def __init__(self, estimator, X, y, scoring=None, cv=None):
79			self.estimator = estimator
80			self.X = X
81			self.y = y
82			self.scoring = scoring
83			self.cv = cv
84
85			super().__init__()
86
87			if cv is None:
88			from sklearn.model_selection import KFold
89
90			self._cv = KFold(n_splits=3, shuffle=True)
91			elif isinstance(cv, int):
92			from sklearn.model_selection import KFold
93
94			self._cv = KFold(n_splits=cv, shuffle=True)
95			else:
96			self._cv = cv
97
98			# check if scoring is a scorer by checking for "estimator" in signature
99			if scoring is None:
100			self._scoring = check_scoring(self.estimator)
101			# check using inspect.signature for "estimator" in signature
102			elif callable(scoring):
103			from inspect import signature
104
105			if "estimator" in signature(scoring).parameters:
106			self._scoring = scoring
107			else:
108			from sklearn.metrics import make_scorer
109
110			self._scoring = make_scorer(scoring)
111			self.scorer_ = self._scoring
112
113			def _paramnames(self):
114			"""Return the parameter names of the search.
115
116			Returns
117			-------
118			list of str
119			The parameter names of the search parameters.
120			"""
121			return list(self.estimator.get_params().keys())
122
123			def _score(self, params):
124			"""Score the parameters.
125
126			Parameters
127			----------
128			params : dict with string keys
129			Parameters to score.
130
131			Returns
132			-------
133			float
134			The score of the parameters.
135			dict
136			Additional metadata about the search.
137			"""
138			estimator = clone(self.estimator)
139			estimator.set_params(**params)
140
141			cv_results = cross_validate(
142			estimator,
143			self.X,
144			self.y,
145			scoring=self._scoring,
146			cv=self._cv,
147			)
148
149			add_info_d = {
150			"score_time": cv_results["score_time"],
151			"fit_time": cv_results["fit_time"],
152			"n_test_samples": _num_samples(self.X),
153			}
154
155			return cv_results["test_score"].mean(), add_info_d
156
157			@classmethod
158			def get_test_params(cls, parameter_set="default"):
159			"""Return testing parameter settings for the skbase object.
160
161			``get_test_params`` is a unified interface point to store
162			parameter settings for testing purposes. This function is also
163			used in ``create_test_instance`` and ``create_test_instances_and_names``
164			to construct test instances.
165
166			``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``.
167
168			Each ``dict`` is a parameter configuration for testing,
169			and can be used to construct an "interesting" test instance.
170			A call to ``cls(**params)`` should
171			be valid for all dictionaries ``params`` in the return of ``get_test_params``.
172
173			The ``get_test_params`` need not return fixed lists of dictionaries,
174			it can also return dynamic or stochastic parameter settings.
175
176			Parameters
177			----------
178			parameter_set : str, default="default"
179			Name of the set of test parameters to return, for use in tests. If no
180			special parameters are defined for a value, will return `"default"` set.
181
182			Returns
183			-------
184			params : dict or list of dict, default = {}
185			Parameters to create testing instances of the class
186			Each dict are parameters to construct an "interesting" test instance, i.e.,
187			`MyClass(params)` or `MyClass(params[i])` creates a valid test instance.
188			`create_test_instance` uses the first (or only) dictionary in `params`
189			"""
190			from sklearn.datasets import load_diabetes, load_iris
191			from sklearn.svm import SVC, SVR
192			from sklearn.metrics import accuracy_score, mean_absolute_error
193			from sklearn.model_selection import KFold
194
195			X, y = load_iris(return_X_y=True)
196			params_classif = {
197			"estimator": SVC(),
198			"scoring": accuracy_score,
199			"cv": KFold(n_splits=3, shuffle=True),
200			"X": X,
201			"y": y,
202			}
203
204			X, y = load_diabetes(return_X_y=True)
205			params_regress = {
206			"estimator": SVR(),
207			"scoring": mean_absolute_error,
208			"cv": 2,
209			"X": X,
210			"y": y,
211			}
212
213			X, y = load_diabetes(return_X_y=True)
214			params_all_default = {
215			"estimator": SVR(),
216			"X": X,
217			"y": y,
218			}
219
220			return [params_classif, params_regress, params_all_default]
221
222			@classmethod
223			def _get_score_params(self):
224			"""Return settings for testing the score function. Used in tests only.
225
226			Returns a list, the i-th element corresponds to self.get_test_params()[i].
227			It should be a valid call for self.score.
228
229			Returns
230			-------
231			list of dict
232			The parameters to be used for scoring.
233			"""
234			score_params_classif = {"C": 1.0, "kernel": "linear"}
235			score_params_regress = {"C": 1.0, "kernel": "linear"}
236			score_params_defaults = {"C": 1.0, "kernel": "linear"}
237			return [score_params_classif, score_params_regress, score_params_defaults]
238

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SklearnCvExperiment.__init__() B last analyzed 2025-06-23 18:24 UTC

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SklearnCvExperiment.init() B
last analyzed 2025-06-23 18:24 UTC