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"""Experiment adapter for sklearn cross-validation experiments.""" |
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from sklearn import clone |
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from sklearn.model_selection import cross_validate |
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from sklearn.utils.validation import _num_samples |
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from hyperactive.base import BaseExperiment |
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class SklearnCvExperiment(BaseExperiment): |
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"""Experiment adapter for sklearn cross-validation experiments. |
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This class is used to perform cross-validation experiments using a given |
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sklearn estimator. It allows for hyperparameter tuning and evaluation of |
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the model's performance using cross-validation. |
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The score returned is the mean of the cross-validation scores, |
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of applying cross-validation to ``estimator`` with the parameters given in |
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``score`` ``params``. |
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The cross-validation performed is specified by the ``cv`` parameter, |
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and the scoring metric is specified by the ``scoring`` parameter. |
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The ``X`` and ``y`` parameters are the input data and target values, |
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which are used in fit/predict cross-validation. |
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Parameters |
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---------- |
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estimator : sklearn estimator |
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The estimator to be used for the experiment. |
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scoring : callable or str |
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sklearn scoring function or metric to evaluate the model's performance. |
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cv : int or cross-validation generator |
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The number of folds or cross-validation strategy to be used. |
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X : array-like, shape (n_samples, n_features) |
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The input data for the model. |
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y : array-like, shape (n_samples,) or (n_samples, n_outputs) |
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The target values for the model. |
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""" |
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def __init__(self, estimator, scoring, cv, X, y): |
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self.estimator = estimator |
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self.X = X |
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self.y = y |
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self.scoring = scoring |
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self.cv = cv |
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def _paramnames(self): |
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"""Return the parameter names of the search. |
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Returns |
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------- |
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list of str |
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The parameter names of the search parameters. |
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""" |
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return list(self.estimator.get_params().keys()) |
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def _score(self, **params): |
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"""Score the parameters. |
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Parameters |
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---------- |
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params : dict with string keys |
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Parameters to score. |
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Returns |
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------- |
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float |
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The score of the parameters. |
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dict |
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Additional metadata about the search. |
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""" |
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estimator = clone(self.estimator) |
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estimator.set_params(**params) |
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cv_results = cross_validate( |
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estimator, |
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self.X, |
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self.y, |
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cv=self.cv, |
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) |
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add_info_d = { |
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"score_time": cv_results["score_time"], |
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"fit_time": cv_results["fit_time"], |
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"n_test_samples": _num_samples(self.X), |
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} |
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return cv_results["test_score"].mean(), add_info_d |
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SimonBlanke /
Hyperactive
