palma.base.splitting_strategy#
Module Contents#
Classes#
Validation strategy for a machine learning project. |
Functions#
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- palma.base.splitting_strategy._index_to_bool(array, length)#
- palma.base.splitting_strategy._bool_to_index(array)#
- class palma.base.splitting_strategy.ValidationStrategy(splitter: sklearn.model_selection._split.BaseShuffleSplit | sklearn.model_selection._split.BaseCrossValidator | List[tuple] | List[str], **kwargs)#
Validation strategy for a machine learning project.
- Parameters:
- - splitter (Union[BaseShuffleSplit, BaseCrossValidator, List[tuple], List[str]]): The data splitting strategy.
- Attributes:
- - test_index (np.ndarray): Index array for the test set.
- - train_index (np.ndarray): Index array for the training set.
- - indexes_val (list): List of indexes for validation sets.
- - indexes_train_test (list): List containing tuples of training and test indexes.
- - id: Unique identifier for the validation strategy.
- - splitter: The data splitting strategy.
Methods
- __call__(X: pd.DataFrame, y: pd.Series, X_test: pd.DataFrame = None, y_test: pd.Series = None, groups=None, **kwargs):
Applies the validation strategy to the provided data.
- property test_index: numpy.ndarray#
- property train_index: numpy.ndarray#
- property indexes_val: list#
- property indexes_train_test: list#
- property id#
- property splitter#
- property groups#
- __call__(X: pandas.DataFrame, y: pandas.Series, X_test: pandas.DataFrame = None, y_test: pandas.Series = None, groups=None, **kwargs)#
Apply the validation strategy to the provided data.
- __correct_nested(X)#
- __str__() str#
Return str(self).