hyperactive.integrations.sklearn.sklearn_cv_experiment.SklearnCvExperiment._score() - Code Metrics - Inspection of "V5 api design" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#101)

by Simon

created 2025-01-27 18:41 UTC

SklearnCvExperiment._score() A

↳ Parent: hyperactive.integrations.sklearn.sklearn_cv_experiment

Complexity

Conditions

Size

Total Lines	32
Code Lines	13

Duplication

Lines	32
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	13
nop	2
dl	32
loc	32
rs	9.75
c	0
b	0
f	0

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

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

from hyperactive.base import BaseExperiment

class SklearnCvExperiment(BaseExperiment):


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

    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(
            self.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


1		"""Experiment adapter for sklearn cross-validation experiments."""
2
3		from sklearn import clone
4		from sklearn.model_selection import cross_validate
5		from sklearn.utils.validation import _num_samples
6
7		from hyperactive.base import BaseExperiment
8
9	View Code Duplication	class SklearnCvExperiment(BaseExperiment):
		0 ignored issues – show Duplication introduced 2025-01-26 08:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
10
11		def __init__(self, estimator, scoring, cv, X, y):
12		self.estimator = estimator
13		self.X = X
14		self.y = y
15		self.scoring = scoring
16		self.cv = cv
17
18		def _paramnames(self):
19		"""Return the parameter names of the search.
20
21		Returns
22		-------
23		list of str
24		The parameter names of the search parameters.
25		"""
26		return list(self.estimator.get_params().keys())
27
28		def _score(self, **params):
29		"""Score the parameters.
30
31		Parameters
32		----------
33		params : dict with string keys
34		Parameters to score.
35
36		Returns
37		-------
38		float
39		The score of the parameters.
40		dict
41		Additional metadata about the search.
42		"""
43		estimator = clone(self.estimator)
44		estimator.set_params(**params)
45
46		cv_results = cross_validate(
47		self.estimator,
48		self.X,
49		self.y,
50		cv=self.cv,
51		)
52
53		add_info_d = {
54		"score_time": cv_results["score_time"],
55		"fit_time": cv_results["fit_time"],
56		"n_test_samples": _num_samples(self.X),
57		}
58
59		return cv_results["test_score"].mean(), add_info_d
60

SimonBlanke / Hyperactive

Pull Request — master (#101)

SklearnCvExperiment._score() A

Complexity

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