glassbox.orchestrator.base_splitter

Abstract BaseSplitter for cross-validation.

---

BaseSplitter

BaseSplitter(n_splits=5, shuffle=False)

Bases: ABC

Abstract base class for cross-validation splitters.

Parameters:

Name	Type	Description	Default
n_splits	int	Number of splits.	5
shuffle	bool	Whether to shuffle data before splitting.	False

Source code in glassbox/orchestrator/base_splitter.py

def __init__(self, n_splits: int = 5, shuffle: bool = False) -> None:
    self.n_splits: int = n_splits
    self.shuffle: bool = shuffle

split abstractmethod

split(X, y)

Generate train/test indices for cross-validation.

Parameters:

Name	Type	Description	Default
X	ndarray	Data array of shape (n_samples, n_features).	required
y	ndarray	Target values of shape (n_samples,).	required

Returns:

Type	Description
Generator[Tuple[ndarray, ndarray], None, None]	Generator yielding training and validation index tuples.

Source code in glassbox/orchestrator/base_splitter.py

@abstractmethod
def split(
    self, X: np.ndarray, y: np.ndarray
) -> Generator[Tuple[np.ndarray, np.ndarray], None, None]:
    """
    Generate train/test indices for cross-validation.

    Parameters
    ----------
    X : np.ndarray
        Data array of shape (n_samples, n_features).
    y : np.ndarray
        Target values of shape (n_samples,).

    Returns
    -------
    Generator[Tuple[np.ndarray, np.ndarray], None, None]
        Generator yielding training and validation index tuples.
    """
    raise NotImplementedError