SSRepeatedKFold.SSRepeatedKFold Class Reference

Public Member Functions
	__init__ (self, n_splits, n_repeats)
	split (self, X, y)

Public Attributes
	labeled_size
	unlabeled_size
	val_size
	test_size
	n_repeats

Detailed Description

    Repeated semi-supervised k-fold cross-validation splitter.

    This class implements a repeated k-fold cross-validation strategy specifically designed for semi-supervised
    learning scenarios. It repeatedly splits the data into labeled, unlabeled, validation, and test sets according
    to the specified sizes.

    Parameters:
    -----------
    - n_splits (tuple): Tuple containing the sizes of labeled, unlabeled, validation, and test sets, respectively.
    - n_repeats (int): Number of times cross-validation should be repeated.

    Attributes:
    -----------
    - labeled_size (int): Size of the labeled set in each split.
    - unlabeled_size (int): Size of the unlabeled set in each split.
    - val_size (int): Size of the validation set in each split.
    - test_size (int): Size of the test set in each split.
    - n_repeats (int): Number of times cross-validation should be repeated.

    Methods:
    -----------
    - split(X, y): Splits the input data into labeled, unlabeled, validation, and test sets.

    Notes:
    -----------
    - This splitter is specifically designed for semi-supervised learning scenarios.

Member Function Documentation

split()

SSRepeatedKFold.SSRepeatedKFold.split	(		self,
			X,
			y )

    Generate indices to split data into labeled, unlabeled, validation, and test sets.

    Parameters:
    -----------
    - X (array-like): Feature matrix.
    - y (array-like): Target labels.

    Returns:
    -----------
    - tuple: A tuple containing indices for labeled, unlabeled, validation, and test sets.

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The documentation for this class was generated from the following file:

• Resources/SSRepeatedKFold.py