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from sklearn.model_selection import cross_val_score |
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from sklearn.ensemble import GradientBoostingRegressor |
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from hyperactive.base import BaseExperiment |
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class SklearnExperiment(BaseExperiment): |
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""" |
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Initializes the SklearnExperiment with the given estimator, data, and cross-validation settings. |
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Parameters |
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---------- |
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estimator : object |
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The machine learning estimator to be used for the experiment. |
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X : array-like |
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The input data for training the model. |
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y : array-like |
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The target values corresponding to the input data. |
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cv : int, optional |
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The number of cross-validation folds (default is 4). |
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""" |
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def __init__(self, estimator, X, y, cv=4): |
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super().__init__() |
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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.cv = cv |
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def _score(self, **params): |
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model = self.estimator(**params) |
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scores = cross_val_score(model, self.X, self.y, cv=self.cv) |
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return scores.mean() |
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class GradientBoostingExperiment(BaseExperiment): |
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""" |
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A class for conducting experiments with Gradient Boosting Regressor using cross-validation. |
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This class inherits from BaseExperiment and allows users to perform experiments |
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with the GradientBoostingRegressor from sklearn. Users can specify the input |
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features, target values, and the number of cross-validation folds. |
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Attributes: |
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estimator (type): The regression model to be used, default is GradientBoostingRegressor. |
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X (array-like): The input features for the model. |
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y (array-like): The target values for the model. |
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cv (int): The number of cross-validation folds. |
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Methods: |
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_score(**params): Evaluates the model using cross-validation and returns the mean score. |
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""" |
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def __init__(self, X, y, cv=4): |
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super().__init__() |
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self.estimator = GradientBoostingRegressor # The user could also predefine the estimator |
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self.X = X |
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self.y = y |
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self.cv = cv |
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def _score(self, **params): |
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model = self.estimator(**params) |
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scores = cross_val_score(model, self.X, self.y, cv=self.cv) |
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return scores.mean() |
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SimonBlanke /
Hyperactive
