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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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from sklearn.utils.metaestimators import available_if |
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from sklearn.utils.deprecation import _deprecate_Xt_in_inverse_transform |
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from sklearn.exceptions import NotFittedError |
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from sklearn.utils.validation import check_is_fitted |
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from .utils import _estimator_has |
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# NOTE Implementations of following methods from: |
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search.py |
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# Tag: 1.5.1 |
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class BestEstimator: |
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@available_if(_estimator_has("score_samples")) |
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def score_samples(self, X): |
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check_is_fitted(self) |
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return self.best_estimator_.score_samples(X) |
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@available_if(_estimator_has("predict")) |
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def predict(self, X): |
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check_is_fitted(self) |
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return self.best_estimator_.predict(X) |
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@available_if(_estimator_has("predict_proba")) |
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def predict_proba(self, X): |
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check_is_fitted(self) |
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return self.best_estimator_.predict_proba(X) |
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@available_if(_estimator_has("predict_log_proba")) |
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def predict_log_proba(self, X): |
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check_is_fitted(self) |
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return self.best_estimator_.predict_log_proba(X) |
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@available_if(_estimator_has("decision_function")) |
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def decision_function(self, X): |
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check_is_fitted(self) |
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return self.best_estimator_.decision_function(X) |
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@available_if(_estimator_has("transform")) |
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def transform(self, X): |
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check_is_fitted(self) |
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return self.best_estimator_.transform(X) |
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@available_if(_estimator_has("inverse_transform")) |
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def inverse_transform(self, X=None, Xt=None): |
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X = _deprecate_Xt_in_inverse_transform(X, Xt) |
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check_is_fitted(self) |
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return self.best_estimator_.inverse_transform(X) |
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@property |
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def classes_(self): |
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_estimator_has("classes_")(self) |
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return self.best_estimator_.classes_ |
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SimonBlanke /
Hyperactive
